Geospatial Artificial Intelligence in Environmental and Natural Resources Management
Geo-information Science, Machine and Deep Learning, and Big Data Analytics
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Beschreibung
This book explores diverse dimensions of geo-intelligence technology in developing a computing framework for natural resource management and research. Tied to the current era of spatial big data challenges and analytical methods, which have become ubiquitously available from various sources (e.g., Natural Earth Data, USGS Earth Explorer, Open Street Map, Google Earth Engine, etc.), there is a need for the use of the latest advances in technology to mitigate and reverse environmental challenges that humanity will continue to face in the coming decades. Geospatial artificial intelligence (GeoAI), which is the integration of geospatial data, spatial analysis, and AI methods, especially machine learning and deep-learning methods, is bringing tremendous opportunities for generating spatial information on natural resources and assessing environmental risks and challenges. It also provides a key reference list for educators, students, researchers, and practitioners to keep up with the latest GeoAI research topics.
This book explores diverse dimensions of geo-intelligence technology in developing a computing framework for natural resource management and research. Tied to the current era of spatial big data challenges and analytical methods, which have become ubiquitously available from various sources (e.g., Natural Earth Data, USGS Earth Explorer, Open Street Map, Google Earth Engine, etc.), there is a need for the use of the latest advances in technology to mitigate and reverse environmental challenges that humanity will continue to face in the coming decades. Geospatial artificial intelligence (GeoAI), which is the integration of geospatial data, spatial analysis, and AI methods, especially machine learning and deep-learning methods, is bringing tremendous opportunities for generating spatial information on natural resources and assessing environmental risks and challenges. It also provides a key reference list for educators, students, researchers, and practitioners to keep up with the latest GeoAI research topics.
Contains updated key references to diverse topics in the field of Earth and Environmental Sciences for the first time Focuses on the use of the latest advances in technolog Provides frameworks/methodologies to the researchers, students, and others
Autor*in
Hamdi Zurqani
Themen in »Geospatial Artificial Intelligence in Environmental and Natural Resources Management«
Goo-intelligence Remote Sensing Geographic Information Systems (GIS) Geospatial Analysis Land Evaluation Change Detection Landscape Degradation