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

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Detalles Bibliográficos
Publicado en:	Remote Sensing vol. 17, no. 3 (2025), p. 550
Autor principal:	Dritsas, Elias
Otros Autores:	Trigka, Maria
Publicado:	MDPI AG
Materias:	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
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	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
Fuente:	Advanced Technologies & Aerospace Database