Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{199722,
        author = {Dr. Shilpi Yadav},
        title = {Geo-AI FOR ENVIRONMENTAL MONITORING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15436-15449},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199722},
        abstract = {GeoAI – the fusion of geospatial information systems (GIS) and artificial intelligence (AI) – is revolutionizing environmental monitoring. By integrating advanced machine learning (ML) and deep learning (DL) methods with diverse spatial data (satellites, drones, sensors, etc.), GeoAI enables automated analysis of ecosystems at unprecedented scale and resolution. In air and water quality, multi-source fusion (satellite +ground) with spatio-temporal neural networks yields highly accurate forecasts of pollutants. For land cover and forestry, deep CNNs and segmentation models (e.g. U-Net, Deep Lab) process Landsat/Sentinel time series to map annual habitat change. In biodiversity, initiatives like NOAA’s GAIA use very-high-resolution satellite imagery plus cloud-based ML to detect whales across oceans. GeoAI also advances disaster risk (floods, wildfires, storms) and urban analysis (heat islands) by extracting features (e.g. thermal indices from ECOSTRESS) and applying hybrid CNN+RNN/Transformer models.
GeoAI workflows involve many data sources and steps: acquiring multi-spectral images (Sentinel-2, Landsat, MODIS, Planet, etc.), LiDAR point clouds, SAR data (Sentinel-1) and in-situ/IoT sensor readings; preprocessing (georeferencing, calibration, cloud masking) and feature engineering (vegetation indices like NDVI, water indices, spatial filters); constructing spatial–temporal models (3D CNNs, ConvLSTM, spatio-temporal GNNs, Transformer-based nets); and deployment (cloud platforms, mobile/edge devices, GIS integration). Open-source libraries (GDAL/Rasterio, PyTorch/TensorFlow) and tools (Google Earth Engine, QGIS, ArcGIS Pro) are used widely. Key challenges remain: heterogeneous data quality (cloud cover, noise), scale (petabytes of imagery), and biases (uneven station coverage) can limit model generalization. Model interpretability and regulatory compliance (e.g. EU AI Act’s transparency requirements) demand explainable AI methods. Privacy and legal issues arise when using high-resolution imagery or mobile data. High-performance computing costs also constrain deployments. Best practices – data augmentation, cross-region validation, XAI auditing, privacy-preserving federated learning – are recommended, and future work is pushing toward large foundation models for geoscience, multimodal sensor fusion, and operational decision-support systems.
This paper provides a comprehensive survey of recent GeoAI methods and applications for environmental monitoring. We define GeoAI’s scope, review AI/GIS techniques, summarize domain applications (air, water, land, biodiversity, disasters, climate, urban heat, agriculture), describe key datasets and sensors, outline common pipelines (with flowcharts and tables), and discuss tools, case studies, and open data. Major references are drawn from the last five years, including seminal journals and official reports.},
        keywords = {Artificial Inteligence (AI); Remote Sensing; Geographical Information System (GIS); Environmental Monitoring.},
        month = {April},
        }