\r\n\tRisk management aims to develop an efficient organizational development environment through risk planning, assessment, analysis, and control. This process will apply in all areas of activity, and the evaluation framework is the same regardless of the field. This volume will aim to appeal to chapters that address methods, models, evaluation frameworks, benefits, barriers, and other dimensions of risk management.
\r\n\tSustainability and the circular economy are approaches approached by many companies and have become activities of global interest. Protecting the environment, streamlining the consumption of organizational resources, reducing the amount of waste generated, and other activities are objectives of these efforts. The circular economy contributes to the sustainable development of the company or country and the achievement of the global objectives of sustainable development. This book will aim to collect various studies for organizational and global sustainability.
\r\n\tLeadership has become a globally desirable approach that can help improve organizational competitiveness and reduce organizational risks. Risks and barriers in risk-free management can be well managed through effective organizational leadership. This book will aim to bring together chapters that explore different areas of leadership.
Typical shortcomings of current image analysis tools are the lack of content understanding. This becomes apparent with current developments in Earth observation and data analysis [1]. In this chapter, we therefore concentrate on artificial intelligence (AI) applications and our solution strategies as our main objectives in the field of remote sensing, i.e., the acquisition and semantic interpretation of instrument data from remote platforms such as aircraft or satellites observing, for instance, atmospheric phenomena on Earth for weather prediction—or icebergs drifting in arctic waters endangering maritime transport. In particular, we will describe the exploitation of imaging data acquired by Earth-observing satellites and their sensors.
\nThese satellites may either circle about the Earth (mostly on low polar Earth orbits) or be operated from stationary or slowly moving points high above our planet (on so-called geostationary or geosynchronous orbits). Typical examples are Earth-observing and meteorological satellites. All these instruments have been designed with dedicated goals that, as a rule, can only be fulfilled by systematic and interactive data processing and data interpretation on the ground. The processing and data analysis chains are then the main candidates where one can and shall apply modern data science approaches (e.g., machine learning and artificial intelligence) in order to fully exploit the full information content of the sensor data.
\nIn general, we have quite a number of different sensors installed on satellites. These include passive instruments observing the backscattered solar illumination or thermal emissions from the Earth—or active imaging instruments (transmitting and receiving light pulses or radio signals toward and from the target area being observed). For the ease of understanding, we will limit ourselves to optical sensors operating in the visible and infrared spectral ranges and to radar sensors applying synthetic-aperture radar (SAR) concepts [2, 3]. These instruments provide large-scale images with a typical spatial resolution of 1–40 m per pixel. The images can be acquired from spacecraft orbits that cover the Earth completely with well-defined repeat cycles.
\nAfter being transmitted to the ground, the image data will have to undergo systematic processing steps. Typically, the processing schemes follow a stepwise approach where for all steps the image data are accompanied by the necessary descriptor data (metadata). The processing chains start with what we call level-0 data consisting of reordered and annotated detector data; level-1 data provide calibrated sensor data, while level-2 data contain data in commonly known physical units preferably on regular spatial or map grids. Then level-3 data are higher-level products such as thematic maps or time series results (obtained by merging or concatenation of several individual images) or similar operations. Finally, users can apply additional interactive processing steps on their own or exploit available software/platform concepts [4].
\nThis principle of ordered value-adding requires well-established techniques for data management, batch processing and databases, local and distributed (cloud) processing, understanding of the information flow, experience with learning principles, knowledge extraction from image and library data, and discovery of image semantics. At present, typical data sources with easy access are publicly available scientific image data provided by the European Copernicus mission with its Sentinel satellites [5, 6] as well as high-resolution remote sensing images [7, 8]. The European Sentinel satellites comprise among others a constellation of SAR imagers (i.e., Sentinel-1A/Sentinel-1B providing typically large radar images, with a ground sampling distance of 20 meters and selectable horizontal and vertical polarizations), and a constellation of optical imagers (i.e., Sentinel-2A/Sentinel-2B delivering typically large multispectral images with 13 different bands and a ground resolution—depending on the bands—of 10–60 m). This space segment of the Copernicus mission is complemented by systematic level-1 and level-2 image data processing on the ground and by support environments that serve as comfortable platforms for further data handling and interpretation covering all aspects of applied data science. These approaches then pave the way for deeper semantic data analysis and understanding as typically required in Earth observation for crop yield predictions, atmospheric research, etc.
\nThe design of Earth observation (EO) missions as constellations of several satellites brings important advantages. However, this is not the case for some of the most popular EO missions. Figure 1 shows typical TerraSAR-X and Copernicus Sentinel overpasses from different orbits and their target areas.
\nSatellite overpasses of Sentinel-1A/Sentinel-1B, Sentinel-2A/Sentinel-2B, and TerraSAR-X (on 23th of August 2018 starting at 14:02 UT) [
TerraSAR-X flies on a polar Sun-synchronous circular dawn-dusk orbit. This satellite shares its orbit plane with its twin satellite TanDEM-X (keeping a 97.44° orbital phasing difference) and a repeat cycle of 11 days with 167 orbits per cycle. Due to its flexibility, TerraSAR-X can cover any point on Earth within a maximum of 4.5 days and 90% of the Earth’s surface within 2 days [9].
\nThe Sentinel-1 satellites fly on a near-polar, Sun-synchronous orbit, too. The satellite constellations (comprising Sentinel-1A and Sentinel-1B) share the same orbit plane with a 180° orbital phasing difference and a repeat cycle of 6 days with 175 orbits per cycle. Sentinel-1 can cover the equator on 3 days, the Artic on less than 1 day, and Europe, Canada, and shipping routes in 1–3 days [10].
\nLike the Sentinel-1 constellation, the Sentinel-2 constellations (comprising Sentinel-2A and Sentinel-2B) share the same orbit with a separation of 180°. The repeat cycle is 5 days with 143 orbits per cycle. Sentinel-2 can cover the equator on 5 days under cloud-free conditions and in 2–3 days at mid-latitudes [11].
\nWhen selecting data for fusion, we have to constrain ourselves to data acquired as close as possible in time.
\nThese data handling approaches are typical for recent advances in big data scenarios in distributed systems on the web (e.g., with high data volumes and throughput rates, conventional and innovative data processing steps, additional necessary tools and environments, and greater user expectations). In our case, this affects the tasks of image processing (e.g., data fusion), image understanding, and comparisons with physical models. This can also be seen when we look at the evolution of satellite data analysis. While early concepts started with data being transferred to algorithms, current systems often transfer data to archives, and future systems may support more and more distributed systems.
\nA typical example is the full functionality offered by machine learning tools, while the basic ideas of future data science aspects for Earth observation as seen by the European Space Agency can be found in [13]. In our case, we are interested in applying more theoretical data science, machine learning, and artificial intelligence (for instance, deep learning, powerful classification maps, and prediction results) together with interactive visualization on various information levels. These ideas will be dealt with below for three remote sensing scenarios as detailed in [14]:
Urban monitoring (urban growth and sprawl, urban classification, and semantic indicators)
Quantitative interpretation of forested areas
Disaster monitoring (earthquakes, inundations, mud slides, etc.)
Here traceable products yielding quantitative data about physical phenomena, change maps, and change predictions are among our primary goals. Of course, we have to consider the implementation effort as well as the attainable accuracy of our products. For each scenario dealt with below, the reader should try to understand what the additional value of machine learning, artificial intelligence, and comprehensive use of data science concepts brings about.
\nThe basic terms of machine learning, artificial intelligence, and data science shall be understood in the following sense:
We use the term “machine learning” mainly when we talk about learning target category parameters derived from selected images and applying these parameters to other examples. Currently, we see much progress by “deep” techniques (e.g., deep learning [15, 16]). An important point is the selection of reliable reference data for traceable validation and verification of the methods.
“Artificial intelligence” describes how machine learning results are exploited for further use. Typically this includes recognizing and being aware of typical situations, making decisions based on the recognized high-level parameters, and predicting future developments. To this end, one can profit from external databases complementing machine learning results.
“Data science” covers the entire field of comprehensive data management and tools, machine learning, and artificial intelligence. This includes topics like distributed processing, monitoring of workflows, visualization techniques, and performance monitoring. Even seemingly trivial tasks (e.g., accessing and handling of data) may belong to data science. However, remote sensing still is in urgent need of efficient tools to familiarize the user community with remote sensing opportunities.
When we look at remote sensing in more detail, we currently see many efforts to transform sensor data to physical quantities that can be exploited for quantitative analysis or modeling. If we accomplish this, we can combine measured data with physical models and find quantitative parameters for predictions.
\nIn the following, we describe how we applied these concepts in a research project funded by the European Union [17]; the project’s main objective is to allow the creation of added value from Copernicus data through the provisioning of modeling and analytics tools for data collection, processing, storage, and access that are provided by the Copernicus Data and Information Access Services (DIAS) [18] and creating a data science workflow where sub-images (image chips) are annotated, administered, and validated based on their assigned semantic labels [19].
\nThe chapter is organized in seven main sections. Section 2 explains the CANDELA platform used for prototyping EO applications, while Section 3 describes the characteristics of the data set. Section 4 presents typical examples which a user can obtain when using the platform from Section 2 and the data set from Section 3. Section 5 illustrates the perspectives in EO data science workflows and Section 6 summarizes our conclusions, while Section 7 contains the future work. The chapter ends with acknowledgments and a list of references.
\nCANDELA’s main objective is the creation of additional value from Copernicus data through the provisioning of modeling and analytics tools provided that the tasks of data collection, processing, storage, and access will be carried out by the Copernicus Data Information and Access Service [18]. The corresponding flowchart is presented in Figure 2 and in [17]. In the end, after the integration of all components, CANDELA will be deployed on top of DIAS.
\nCANDELA platform [
The CANDELA platform [17] allows prototyping of EO applications by applying efficient data retrieval, data mining augmented with machine learning techniques, as well as interoperability in order to fully benefit from the available assets and to add more value to the satellite data. It also helps to interactively detect objects or structures and to classify land cover categories.
\nThe implementation of the platform is putting in place a set of powerful tools in artificial intelligence environments (e.g., with machine learning and deep learning). These tools have as their objectives:
To process large volumes of EO data and to perform data analytics
To extract the information content from the EO data based on data mining
To fuse various EO sensors in order to increase and to complement the information extracted from different sensors
To apply deep learning to detect changes in EO data
To semantically search and index our EO image catalog
From this list of objectives, we focus on two of them, namely, data mining and data fusion (see Figure 3). Our goal is to simplify data access and to analyze large volumes of EO data without specific knowledge about the processing of EO data and to fuse the outputs for content exploration.
\nBlock diagram of the CANDELA platform modules [
For the development of the data mining component, we started from [20], and we improved the cascaded active learning system of [21] for typical Copernicus Earth observation images. Its implementation, test, and validation aim at automated knowledge extraction and image content interpretation. The results are presented in Section 4.1.
\nRegarding data fusion, a new sub-component had to be developed within data mining. This new sub-component fuses multispectral and SAR images. There are two types of fusions; one is performed at the feature level and the other one at the semantic level. The results are shown in Section 4.2 for feature-level fusion.
\nOur main data sets extracted from different instruments are Earth’s surface images of the European Copernicus Programme (e.g., Sentinel-1 and Sentinel-2). Sentinel-1 is a twin satellite synthetic-aperture radar configuration, while Sentinel-2 is also a twin satellite configuration, each carrying a multispectral imager [22, 23].
\nThere are three reasons why we are selecting and using Sentinel-1 and Sentinel-2 images. Firstly, we can recognize different target area details in overlapping radar and optical images complementing each other with rapid succession. Secondly, individually selectable Sentinel-1 and Sentinel-2 images can be rectified and co-aligned by publicly available toolbox routines offered by ESA allowing a straightforward image comparison or image fusion. Thirdly, all Sentinel instruments are totally openly available to the EO community. Many publications (dedicated conferences [1, 24, 25, 26]) already describe newly discovered Earth’s surface characteristics derived from the individual instruments.
\nFurthermore, the long-term operations of the Sentinel satellites allow the interpretation of image time series or even the combination of time series data with external supplementary data via additional data mining and data fusion tools [1, 25, 26].
\nBesides these data sets, we include other third-party EO mission data sets as specified by CANDELA users (e.g., TerraSAR-X and WorldView).
\nThe Sentinel-1 mission comprises a constellation of two satellites (launched on April 1, 2014, and on April 25, 2016), operating in C-band for synthetic-aperture radar imaging. SAR has the advantage of operating at wavelengths not impeded by thin cloud cover, or a lack of solar illumination, and can acquire data over a selected area during day- or nighttime under nearly no weather condition restrictions. The repeat period of each satellite is 12 days; that means every 6 days there is an acquisition by one of the two satellites.
\nThe Sentinel-1 characteristics are presented in detail in [22]. From the multitude of parameters/configurations that exist for Sentinel-1, we have selected as examples the following configurations based on data availability, the CANDELA use cases, and our previous experiments: level-1 Ground Range Detected (GRD) products with high resolution (HR) taken routinely in Interferometric Wide (IW) swath mode. These products/data are produced (prior to geo-coding) with a pixel spacing of 10 × 10 m and correspond to about five looks and a resolution (range × azimuth) of 20 × 22 m. They have a nearly uniform signal-to-noise ratio (SNR) and also a stable distributed target ambiguity ratio (DTAR). For these products, the data are provided in dual polarization, VV and VH for land and HH and HV for polar target areas.
\nThe Sentinel-2 mission (like Sentinel-1) comprises a constellation of two satellites (launched on June 23, 2015, and on March 7, 2017) able to collect multispectral data and is affected by the weather conditions (e.g., cloud cover). The repeat period of each satellite is 10 days; that means every 5 days there is an acquisition of one of the two satellites, thus providing a high revisit frequency.
\nEach Sentinel-2 satellite carries a multispectral instrument with 13 spectral channels (in the visible/near-infrared and shortwave infrared spectral range) and with 290 km swath width. The Sentinel-2 characteristics are presented in detail in [23]. This also applies to level-1 data; level-1C of these products are radiometrically and geometrically corrected images with orthorectification and spatial registration on a global reference system with sub-pixel accuracy. Since the product size is very large, each image is divided into several quadrants in UTM WGS84 projection. The average size of a quadrant is 10,980 × 10,980 pixels (rows × columns). For visualization, the RGB bands (B04, B03, and B02) were used to generate a quick-look quadrant image. For feature extraction, the user can choose different band combinations.
\nFrom the available third-party mission data sets, we selected for demonstration four pairs of multi-sensor images of TerraSAR-X and WorldView-2 [27].
\nTerraSAR-X is a German radar satellite launched in June 2007, followed by its TanDEM-X twin in 2010. Both operate in X-band and are side-looking SAR instruments that offer a wide selection of operating modes and product generation options [7]. TerraSAR-X has a revisit cycle of 11 days on the Earth’s equator. We selected high-resolution spotlight mode images because they provide the highest-resolution data of the target areas. As for the product generation options, we took enhanced ellipsoid corrected (EEC) and radiometrically enhanced (RE) data. Finally, we took horizontally polarized (HH) or vertically polarized (VV) images, as this option is most frequently used. The images have a pixel spacing of 1.25 m and a resolution of 2.9 m with WGS-84 map projection. The average size of the images is 8000 rows × 9600 columns.
\nIn contrast, WorldView-2 provides a single panchromatic band and eight multispectral bands. It was launched in October 2009 to become a DigitalGlobe satellite. The revisit period of the satellite is about 3 days on the Earth’s equator [28]. The resolution for the panchromatic band is 0.46 m and for multispectral bands is 1.87 m. The map projection of WorldView-2 is, again, WGS-84, and the size of these images (on average) for panchromatic images is 47,000 × 37,000 pixels (rows × columns) and for multispectral images is 11,000 × 9000 pixels (rows × columns).
\nIn EO data mining, a number of researchers have already developed technologies for semantic image understanding [29, 30]. The available web engines are focused on the everyday needs of a broad category of users [31]. A very popular satellite image data mining system is Tomnod from DigitalGlobe or Google Earth, which is targeting general user topics. Especially for EO, there are systems such as LandEX [32] which is a land cover management system, while GeoIRIS [33] is a system that allows the user to refine a given query by iteratively specifying a set of relevant and a set of nonrelevant images. A similar system is IKONA [34] which is using relevance feedback in order to analyze the content of very high-resolution EO images. Further, the knowledge-driven information mining (KIM) system [41] is an example of an active learning system providing semantic interpretation of image content. The KIM concept evolved into the TELEIOS prototype [36], complementing the scope of searching EO images with additional geo-information and in situ data. Finally, a cascaded active learning prototype [21] has been integrated into an operational EO system [20] to interpret the archives of TerraSAR-X images [37].
\nCANDELA is improving this cascaded active learning system by searching for dedicated algorithms for typical Earth observation images. Its implementation, test, and validation aim at automated knowledge extraction and image content interpretation. The targeted performance characteristics are verified for several typical use cases and tell us more about the potential of dedicated algorithms with respect to general machine learning.
\nFigures 4
TerraSAR-X image of Venice, Italy: (left) a quick-look view of the image and (right) the corresponding classification map generated by CANDELA.
Sentinel-1 image of Venice, Italy (after selecting the area that is covered by TerraSAR-X from the full Sentinel-1 image): (bottom-left) a quick-look view of the image and (bottom-right) the classification map generated by CANDELA.
Classification accuracy (precision/recall) by comparison between TerraSAR-X (top-left) and Sentinel-1 (bottom-right) for the Venice image.
TerraSAR-X image of Munich, Germany: (left) a quick-look view of the image and (right) the classification map generated by CANDELA.
Sentinel-1 image of Munich, Germany (after selecting the area that is also covered by TerraSAR-X): (bottom-left) a quick-look view of the image and (bottom-right) the classification map generated by CANDELA.
Classification accuracy (precision/recall) by comparison between TerraSAR-X (top-right) and Sentinel-1 (bottom-left) for the Munich image.
Sentinel-2 quadrant image of an area of the Dutch Wadden Sea: (left) a quick-look view of the image and (right) the classification map generated by CANDELA.
Classification accuracy (precision/recall) for the Sentinel-2 quadrant image covering an area of the Wadden Sea.
Currently, what exists in the field of data fusion is a collection of routines/algorithms that can be linked and embedded for various applications. A very well-known open-source toolbox is Orfeo [38] which provides a large number of state-of-the-art algorithms to process SAR and multispectral images for different applications. Another one is Google Earth [31] that includes a large image database and an expandable number of algorithms that can be used for image processing.
\nIn our case, we need to recognize different target area details in overlapping SAR and multispectral images. For doing this, we selected a number of cities from all over the world. The cities are Bucharest in Romania, Munich in Germany, Venice in Italy, and Washington in the USA. The selection criteria of these cities were the simultaneous availability of these cities covered by the two satellites and the variety of categories that can be found. A difficulty arises when trying to co-align these images, for example, images provided by TerraSAR-X and WorldView-2, because the original data have different pixel spacing. To solve this problem, we resampled the panchromatic WorldView-2 image in order to co-align it with the TerraSAR-X image [27].
\nIn the case of Sentinel-1 and Sentinel-2, the images can be rectified and co-aligned by publicly available toolbox routines [39]; this allowed us a straightforward image comparison.
\nWhile we are accustomed to image fusion as a radiometric combination of multispectral images, a comparably mature level of semantic fusion of SAR images has not been reached yet. In order to remedy the situation, we propose a semantic fusion concept for SAR images, where we combine the semantic image content of two data sets with different characteristics. By exploiting the specific imaging details and the retrievable semantic categories of the two image types, we obtained semantically fused image classification maps that allow us to differentiate between different categories.
\nFigures 12–14 present the classification maps for each sensor and the fused ones together with their accuracy (e.g., precision/recall) for the city of Venice, while Figures 15–17 apply to the city of Munich.
\nA multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and TerraSAR-X image (bottom-center) for the city of Venice, Italy.
Classification maps generated using the CANDELA platform for the city of Venice: multispectral image (top-left side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all three images (bottom-right side).
Classification accuracy (precision/recall) for a selected image taken over the area of Venice using multispectral, panchromatic, and SAR images and also the fused image.
A multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and TerraSAR-X image (bottom-center) for the city of Munich, Germany.
Classification maps generated using the CANDELA platform for the city of Munich: multispectral image (top-left side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all three images (bottom-right side).
Classification accuracy (precision/recall) for a selected image over the area of Munich using multispectral, panchromatic, and SAR images and also the fused image.
For a quantitative assessment, we compared the semantic annotation results with the given reference data set and computed precision/recall for each category and sensor. Analyzing the figures separately, we observed that the average of precision/recall obtained for fused sensor images is higher than the precision/recall of individual sensor images. Unfortunately, there are also cases in which for corresponding image patches tiled from different sensor images, the WorldView-2 annotations have a different semantic classification when compared to the TerraSAR-X results or when a category is missing for one sensor. In our case, in the Venice image, the category “buoys” is only detected in the TerraSAR-X image, and not in the WorldView-2 image. This has a noticeable impact on the performance of the category “boats.” Another example is the category “clouds” that appears in the case of the Munich image that is detected in the WorldView-2 image, but not in the TerraSAR-X image.
\nRecently, a new paradigm for Earth observation, namely, Data Knowledge Discovery, was introduced [17]. This paradigm defines the entire chain “data-information-knowledge-value” and deals with a meaningful EO content extraction, i.e., the semantic and knowledge aspects.
\nWe developed user-invariant and EO domain-specific compensatory methods for the individual user- and domain-subjective biases. The derived models generate a sharable knowledge body as a means to enable the communication between fragmented knowledge learned from metadata, image data, and other data in synergy with the domain expertise of EO users. Today’s EO paradigms and technologies are largely domain-oriented and have to support the communication outlined above.
\nArtificial intelligence big data in Earth observation [13] forced the development of new technologies starting from management platforms [4] and is reaching now the information platforms.
\nAn example for the first category are ESA’s Thematic Exploitation Platforms (TEPs) [4] that are designed and focused for coastal applications, forest, geohazards, hydrology, polar, urban, and food and security application domains, integrating standard processing chains that have low user interaction. The Copernicus system (currently still under development) and its data information and access services component [18] are a major achievement but still represent a “classic” management paradigm.
\nCurrently, “classic” existing systems/platforms are usually batch-oriented (e.g., TEPs, DIAS), but with EOLib [20, 40] and the new CANDELA platform [17], this paradigm was “moved” to interactive systems (e.g., supporting active learning).
\nThere are three perspectives to describe this type of interactive systems:
The objective is the knowledge extraction from the sensor signal of the physically meaningful parameters or Earth’s surface cover categories.
The process is divided in two steps:
The first step is an automated batch process to manage the satellite image product files, i.e., to extract the image data and to select the relevant metadata, to perform a spatial breakdown of the image into patches, to estimate for each image patch the particular signatures or primitive descriptors, and to further structure the extracted information in a database.
In a second step following interactive machine learning paradigms, the extracted information is transformed into semantic entities attached to each image location. The process is a combination of querying, browsing, and active learning. Using positive examples, i.e., training samples for the categories of interest and complemented by negative examples to enhance the accuracies of each class, a user can define the image semantics adapted to a particular application.
Based on these procedures, value-adding is an iterative process.
The satellite data are generally multi-mission data, e.g., multispectral and SAR data that are restructured in a common database, which becomes the
The signal-information logic scheme: chain data-information-knowledge.
The signal-information logic scheme: chain data-information-knowledge-semantic value.
The value-adding logic scheme.
Among them are the generation of radiometrically and geometrically calibrated data cubes.
The implementation of these paradigms requires a concept of integrating artificial intelligence with software (SW) system architectures enabling interactive multiuser operations in real time relative to the user reaction times. End users will be able to work on shared user scenarios, results of their analyses, or information extraction procedures.
The central component is a
The
A deep neural network (
The logic
The extracted information and data content are again indexed in the DI and provided (via web services) to one of the four human-machine interface (HMI) modules (i.e.,
The advantages and benefits of the proposed approach are:
We do clustering considering the physical parameters behind the sensors contrary with the classical classification proposed in AI.
With very few examples, we are able to classify the images with high accuracy.
We are able to process multi-sensor data.
We are able to create a semantic scheme adapted to different EO sensors (SAR or multispectral), high resolution (e.g., TerraSAR-X or WorldView)/medium resolution (e.g., Sentinel-1 or Sentinel-2).
During the next years, we expect a wide variety of new satellite image data that can be easily downloaded, handled, and analyzed by individual users. We also think that a number of new geophysical databases and browse tools will become available so that each user has easy access to numerous additional satellite data sources together with auxiliary geophysical data from common libraries and data management tools supporting in-depth image data analyses and their interpretation. Innovative application fields (such as autonomous driving based on machine learning and artificial intelligence) will bring us still more data handling tools and new data archives becoming available via the Internet. In addition, we also suppose that these new tools will be supplemented by management and support environments, for instance, for system testing and performance monitoring. Within the next 5 years, this should result in new established environments for image data understanding.
\nPart of this work was supported by CANDELA—the Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator—a H2020 research and innovation project under grant agreement no. 776193.
\nAnother part of the work was supported by EOLib—the Earth Observation Image Librarian—an ESA technological project.
\nThe TerraSAR-X image data being used in this study were provided by the TerraSAR-X Science Service System (Proposal MTH 1118), while the WorldView-2 image data were provided by the European Space Imaging (EUSI).
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Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón Poggi",slug:"juan-carlos-gardon-poggi",fullName:"Juan Carlos Gardón Poggi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:null,institution:{name:"Valencia Catholic University Saint Vincent Martyr",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",country:{name:"United Kingdom"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",biography:"Samir El-Gendy is a Professor of anatomy and embryology at the faculty of veterinary medicine, Alexandria University, Egypt. Samir obtained his PhD in veterinary science in 2007 from the faculty of veterinary medicine, Alexandria University and has been a professor since 2017. Samir is an author on 24 articles at Scopus and 12 articles within local journals and 2 books/book chapters. His research focuses on applied anatomy, imaging techniques and computed tomography. Samir worked as a member of different local projects on E-learning and he is a board member of the African Association of Veterinary Anatomists and of anatomy societies and as an associated author at local and international journals. Orcid: https://orcid.org/0000-0002-6180-389X",institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"246149",title:"Dr.",name:"Valentina",middleName:null,surname:"Kubale",slug:"valentina-kubale",fullName:"Valentina Kubale",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246149/images/system/246149.jpg",biography:"Valentina Kubale is Associate Professor of Veterinary Medicine at the Veterinary Faculty, University of Ljubljana, Slovenia. Since graduating from the Veterinary faculty she obtained her PhD in 2007, performed collaboration with the Department of Pharmacology, University of Copenhagen, Denmark. She continued as a post-doctoral fellow at the University of Copenhagen with a Lundbeck foundation fellowship. She is the editor of three books and author/coauthor of 23 articles in peer-reviewed scientific journals, 16 book chapters, and 68 communications at scientific congresses. Since 2008 she has been the Editor Assistant for the Slovenian Veterinary Research journal. She is a member of Slovenian Biochemical Society, The Endocrine Society, European Association of Veterinary Anatomists and Society for Laboratory Animals, where she is board member.",institutionString:"University of Ljubljana",institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",biography:"Dr. Fonseca-Alves earned his DVM from Federal University of Goias – UFG in 2008. He completed an internship in small animal internal medicine at UPIS university in 2011, earned his MSc in 2013 and PhD in 2015 both in Veterinary Medicine at Sao Paulo State University – UNESP. Dr. Fonseca-Alves currently serves as an Assistant Professor at Paulista University – UNIP teaching small animal internal medicine.",institutionString:null,institution:{name:"Universidade Paulista",country:{name:"Brazil"}}},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",biography:"María de la Luz García Pardo is an agricultural engineer from Universitat Politècnica de València, Spain. She has a Ph.D. in Animal Genetics. Currently, she is a lecturer at the Agrofood Technology Department of Miguel Hernández University, Spain. Her research is focused on genetics and reproduction in rabbits. The major goal of her research is the genetics of litter size through novel methods such as selection by the environmental sensibility of litter size, with forays into the field of animal welfare by analysing the impact on the susceptibility to diseases and stress of the does. Details of her publications can be found at https://orcid.org/0000-0001-9504-8290.",institutionString:null,institution:{name:"Miguel Hernandez University",country:{name:"Spain"}}},{id:"350704",title:"M.Sc.",name:"Camila",middleName:"Silva Costa",surname:"Ferreira",slug:"camila-ferreira",fullName:"Camila Ferreira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/350704/images/17280_n.jpg",biography:"Graduated in Veterinary Medicine at the Fluminense Federal University, specialist in Equine Reproduction at the Brazilian Veterinary Institute (IBVET) and Master in Clinical Veterinary Medicine and Animal Reproduction at the Fluminense Federal University. She has experience in analyzing zootechnical indices in dairy cattle and organizing events related to Veterinary Medicine through extension grants. I have experience in the field of diagnostic imaging and animal reproduction in veterinary medicine through monitoring and scientific initiation scholarships. I worked at the Equus Central Reproduction Equine located in Santo Antônio de Jesus – BA in the 2016/2017 breeding season. I am currently a doctoral student with a scholarship from CAPES of the Postgraduate Program in Veterinary Medicine (Pathology and Clinical Sciences) at the Federal Rural University of Rio de Janeiro (UFRRJ) with a research project with an emphasis on equine endometritis.",institutionString:null,institution:null},{id:"41319",title:"Prof.",name:"Lung-Kwang",middleName:null,surname:"Pan",slug:"lung-kwang-pan",fullName:"Lung-Kwang Pan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41319/images/84_n.jpg",biography:null,institutionString:null,institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain.Dr. Satué is accredited as a Private University Doctor Professor, Doctor Assistant, and Contracted Doctor by AVAP (Agència Valenciana d'Avaluació i Prospectiva) and currently, as a full professor by ANECA (since January 2022). To date, Katy has taught 22 years in the Department of Animal Medicine and Surgery at the CEU-Cardenal Herrera University in undergraduate courses in Veterinary Medicine (General Pathology, integrated into the Applied Basis of Veterinary Medicine module of the 2nd year, Clinical Equine I of 3rd year, and Equine Clinic II of 4th year). Dr. Satué research activity is in the field of Endocrinology, Hematology, Biochemistry, and Immunology in the Spanish Purebred mare. She has directed 5 Doctoral Theses and 5 Diplomas of Advanced Studies, and participated in 11 research projects as a collaborating researcher. She has written 2 books and 14 book chapters in international publishers related to the area, and 68 scientific publications in international journals. Dr. Satué has attended 63 congresses, participating with 132 communications in international congresses and 19 in national congresses related to the area. Dr. Satué is a scientific reviewer for various prestigious international journals such as Animals, American Journal of Obstetrics and Gynecology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology, among others. Since 2014 she has been responsible for the Clinical Analysis Laboratory of the CEU-Cardenal Herrera University Veterinary Clinical Hospital.",institutionString:null,institution:null},{id:"201721",title:"Dr.",name:"Beatrice",middleName:null,surname:"Funiciello",slug:"beatrice-funiciello",fullName:"Beatrice Funiciello",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201721/images/11089_n.jpg",biography:"Graduated from the University of Milan in 2011, my post-graduate education included CertAVP modules mainly on equines (dermatology and internal medicine) and a few on small animal (dermatology and anaesthesia) at the University of Liverpool. After a general CertAVP (2015) I gained the designated Certificate in Veterinary Dermatology (2017) after taking the synoptic examination and then applied for the RCVS ADvanced Practitioner status. After that, I completed the Postgraduate Diploma in Veterinary Professional Studies at the University of Liverpool (2018). My main area of work is cross-species veterinary dermatology.",institutionString:null,institution:null},{id:"291226",title:"Dr.",name:"Monica",middleName:null,surname:"Cassel",slug:"monica-cassel",fullName:"Monica Cassel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/291226/images/8232_n.jpg",biography:'Degree in Biological Sciences at the Federal University of Mato Grosso with scholarship for Scientific Initiation by FAPEMAT (2008/1) and CNPq (2008/2-2009/2): Project \\"Histological evidence of reproductive activity in lizards of the Manso region, Chapada dos Guimarães, Mato Grosso, Brazil\\". Master\\\'s degree in Ecology and Biodiversity Conservation at Federal University of Mato Grosso with a scholarship by CAPES/REUNI program: Project \\"Reproductive biology of Melanorivulus punctatus\\". PhD\\\'s degree in Science (Cell and Tissue Biology Area) \n at University of Sao Paulo with scholarship granted by FAPESP; Project \\"Development of morphofunctional changes in ovary of Astyanax altiparanae Garutti & Britski, 2000 (Teleostei, Characidae)\\". She has experience in Reproduction of vertebrates and Morphology, with emphasis in Cellular Biology and Histology. She is currently a teacher in the medium / technical level courses at IFMT-Alta Floresta, as well as in the Bachelor\\\'s degree in Animal Science and in the Bachelor\\\'s degree in Business.',institutionString:null,institution:null},{id:"442807",title:"Dr.",name:"Busani",middleName:null,surname:"Moyo",slug:"busani-moyo",fullName:"Busani Moyo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Gwanda State University",country:{name:"Zimbabwe"}}},{id:"439435",title:"Dr.",name:"Feda S.",middleName:null,surname:"Aljaser",slug:"feda-s.-aljaser",fullName:"Feda S. Aljaser",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"423023",title:"Dr.",name:"Yosra",middleName:null,surname:"Soltan",slug:"yosra-soltan",fullName:"Yosra Soltan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"349788",title:"Dr.",name:"Florencia Nery",middleName:null,surname:"Sompie",slug:"florencia-nery-sompie",fullName:"Florencia Nery Sompie",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sam Ratulangi University",country:{name:"Indonesia"}}},{id:"428600",title:"MSc.",name:"Adriana",middleName:null,surname:"García-Alarcón",slug:"adriana-garcia-alarcon",fullName:"Adriana García-Alarcón",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"428599",title:"MSc.",name:"Gabino",middleName:null,surname:"De La Rosa-Cruz",slug:"gabino-de-la-rosa-cruz",fullName:"Gabino De La Rosa-Cruz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"428601",title:"MSc.",name:"Juan Carlos",middleName:null,surname:"Campuzano-Caballero",slug:"juan-carlos-campuzano-caballero",fullName:"Juan Carlos Campuzano-Caballero",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}}]}},subseries:{item:{id:"7",type:"subseries",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11403,editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",slug:"slawomir-wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",biography:"Professor Sławomir Wilczyński, Head of the Chair of Department of Basic Biomedical Sciences, Faculty of Pharmaceutical Sciences, Medical University of Silesia in Katowice, Poland. His research interests are focused on modern imaging methods used in medicine and pharmacy, including in particular hyperspectral imaging, dynamic thermovision analysis, high-resolution ultrasound, as well as other techniques such as EPR, NMR and hemispheric directional reflectance. Author of over 100 scientific works, patents and industrial designs. Expert of the Polish National Center for Research and Development, Member of the Investment Committee in the Bridge Alfa NCBiR program, expert of the Polish Ministry of Funds and Regional Policy, Polish Medical Research Agency. 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