GeoAI and Foundation Models for Ecological Remote Sensing and Sustainable Ecosystem Management

The accelerating pace of global environmental change driven by climate change, biodiversity loss, land degradation, and urbanization demands transformative approaches to ecosystem monitoring and sustainable resource management. Recent advances in Earth observation technologies, together with rapid progress in Artificial Intelligence (AI), GeoAI, and geospatial foundation models, are revolutionising our ability to observe, understand, and manage Earth's ecosystems at unprecedented spatial and temporal scales.

This Research Topic aims to bring together cutting-edge research at the intersection of remote sensing, ecology, geospatial artificial intelligence, and environmental data science to advance ecological sustainability. The integration of AI-driven analytics with multi-source Earth observation data including satellite platforms (e.g., Sentinel, Landsat, MODIS), UAVs, LiDAR, Synthetic Aperture Radar (SAR), hyperspectral, and thermal sensors enables automated feature extraction, land-cover mapping, predictive modelling, data fusion, and near-real-time environmental monitoring.

Recent advances in machine learning, deep learning, transformer architectures, and geospatial foundation models have significantly improved the ability to capture complex spatial and temporal ecological dynamics. These innovations support applications including biodiversity conservation, forest and carbon monitoring, ecosystem restoration, land degradation assessment, precision agriculture, water resource management, climate adaptation, disaster monitoring, and sustainable urban planning.

Despite these advances, challenges remain including data interoperability, computational scalability, interpretability, uncertainty quantification, transferability, reproducibility, and integration of heterogeneous datasets. Addressing these challenges is essential for trustworthy and operational AI solutions for environmental monitoring and policy support.

This Research Topic welcomes original research articles, reviews, methods papers, and case studies leveraging geospatial foundation models, multimodal learning, cloud-native geospatial computing, and explainable AI for ecosystem monitoring and sustainability.

Topics of Interest

Machine Learning and AI for Ecosystem Monitoring

• Machine learning, deep learning, and foundation models for land cover mapping, vegetation analysis, habitat modelling

• Automated feature extraction, object detection, and spatio-temporal change analysis using satellite and UAV imagery

Remote Sensing for Carbon, Biodiversity, and Ecosystem Services

• Forest biomass and carbon stock estimation

• Biodiversity monitoring, ecosystem service assessment, ecological restoration

• Forest degradation, vegetation dynamics, carbon cycle modelling

Climate Change and Environmental Monitoring

• AI-powered assessment of climate impacts on ecosystems

• Early warning systems for wildfire, drought, desertification, floods, and dust storms

• Ecosystem resilience and climate adaptation

GeoAI for Sustainable Land, Water, and Agriculture

• Precision agriculture and smart water resource management

• Land degradation, desertification, soil moisture monitoring

• Multi-source Earth observation for sustainable natural resource management

Urban Ecology and Sustainable Cities

• Urban heat islands, green infrastructure, urban biodiversity

• Air quality, ecosystem services, environmental justice

• Nature-based solutions and climate-resilient cities

Methodological Innovations in GeoAI

• Multi-sensor and multimodal Earth observation data fusion

• Geospatial foundation models, transformers, explainable AI (XAI)

• Uncertainty quantification, cloud-native geospatial computing, scalable AI workflows

• Trustworthy, ethical, reproducible AI for environmental applications

Science, Policy, and Open Environmental Intelligence

• Translating AI-powered remote sensing into policy and decision support

• Open science, FAIR data principles, reproducible geospatial research

• Citizen science and participatory sensing for environmental monitoring

Rationale and Impact

This Research Topic aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 13 (Climate Action), SDG 15 (Life on Land), SDG 11 (Sustainable Cities and Communities), and SDG 6 (Clean Water and Sanitation). By integrating GeoAI, foundation models, and Earth observation, this collection will synthesize current advances and challenges in AI-driven ecological remote sensing. The emphasis on methodological innovation and applied case studies ensures relevance for researchers, practitioners, and policymakers working toward climate resilience, biodiversity conservation, ecosystem restoration, and sustainable development.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: GeoAI, Machine Learning, Ecological Sustainability, Deep Learning, Foundation Models, Urban Sustainability, Time Series Analysis, Earth Observation;, Multi-Sensor Data Fusion, Climate Adaptation, Biodiversity Conservation

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.