AI and Remote Sensing Synergy: Advancing Environmental Monitoring and Data Analysis

This Research Topic provides a platform for exploring the integration of artificial intelligence and remote sensing technologies to enhance environmental monitoring and data analysis. With the increasing availability of high-resolution satellite imagery and advanced sensor data, there is a growing opportunity to leverage AI-driven methodologies to address complex environmental challenges. Contributions may address the development of novel machine learning models, such as deep learning architectures, for improved accuracy in land cover classification, wildfire risk prediction, and sea ice concentration mapping. Additionally, this topic invites research on uncertainty quantification and the fusion of multimodal data sources to enhance decision support systems. By connecting theoretical advancements with practical applications, this Research Topic aims to foster interdisciplinary collaboration and promote sustainable development goals, including climate action and life on land. Researchers are encouraged to share insights on the use of geographical information systems (GIS) and spatial information technologies to support environmental informatics, ultimately contributing to a deeper understanding of our planet's dynamic systems. This Research Topic welcomes original research applying AI and geospatial technologies to environmental and Earth observation challenges, including, but not limited to:• Land use, land cover, and change detection – AI-based classification, segmentation, and multitemporal analysis of urban, agricultural, and forest landscapes• Environmental and climate monitoring – ecosystem degradation, climate-impact modeling, drought/heatwave forecasting, and carbon estimation• Biodiversity and habitat monitoring – species distribution modeling, conservation prioritization, and invasive species detection• Hazard and risk applications – wildfire, flood, landslide, and multi-hazard prediction, damage mapping, and early-warning systems• Water, coastal, and cryosphere monitoring – hydrology, water quality, shoreline change, sea-ice, and glacier dynamics• Agriculture and food security – crop monitoring, yield prediction, and precision agriculture• Pollution and environmental health – air/water/soil contamination and exposure risk mapping• AI/ML methods for Earth observation – deep learning, foundation models, data fusion, uncertainty quantification, and explainable AI• Operational and policy applications – GeoAI decision support, real-time monitoring (UAV/edge AI), and technology transfer to inform sustainable developmentCross-cutting priorities: interdisciplinary approaches, reproducibility and open data, performance in low-resource settings, and demonstrated real-world environmental or policy impact. Topic editors: Sushant K Singh, Robert Taylor Source: https://www.frontiersin.org/research-topics/84538/ai-and-remote-sensing-synergy-advancing-environmental-monitoring-and-data-analysis