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Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey

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Pubblicato in:Remote Sensing vol. 17, no. 3 (2025), p. 550
Autore principale: Dritsas, Elias
Altri autori: Trigka, Maria
Pubblicazione:
MDPI AG
Soggetti:
Environmental monitoring
Spatial analysis
Artificial intelligence
Datasets
Urban planning
Big Data
Spatiotemporal data
Artificial neural networks
Remote sensing
Disaster management
Data processing
Unmanned aerial vehicles
Data analysis
Data integration
Long short-term memory
Machine learning
Emergency preparedness
Internet of Things
Case studies
Environmental management
Spatial data
Infrastructure
Cloud computing
Neural networks
Sensors
Surveys
Urban areas
Satellites
Decision making
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Abstract:The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making.
ISSN:2072-4292
DOI:10.3390/rs17030550
Fonte:Advanced Technologies & Aerospace Database