Posted inEnvironmental & Material Sciences │ Volume 4 Issue 4

Remote Sensing in The Ai Era, A Review from Data Acquisition to Change Detection

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Publication: 1/7/2026

Page: 1-10

Volume 4 Issue 4

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Jerry, S. H., Dzarma, S. M. (2026). Remote Sensing in The Ai Era, A Review from Data Acquisition to Change Detection. IRESPUB Journal of Environmental & Material Sciences, 4(4), 1-10. https://doi.org/10.62179/irespub-jems.v4i4.1

Samuel Hyellamada Jerry & S. M. Dzarma

Department of Geography and Environmental Sciences Adamawa State University, Mubi

 
Abstract

Remote sensing has become a central methodology for monitoring the Earth’s surface, atmosphere, and oceans, providing spatially explicit, repetitive, and synoptic observations from the field to the global scale. The discipline now encompasses an integrated chain of data models governing acquisition, preprocessing, classification, change detection, and analysis of remotely sensed imagery. This review synthesises the state of the art across this chain, emphasising developments since 2020. It examines radiometric and geometric calibration, atmospheric correction, and modern preprocessing workflows; assesses the transition from statistical classifiers to convolutional neural networks, vision transformers, and geospatial foundation models such as Prithvi-EO and the Clay Foundation Model; and surveys time-series change-detection algorithms (CCDC, LandTrendr, BFAST) alongside deep-learning bi-temporal approaches. Spectral, textural, and geostatistical techniques are reviewed in the context of contemporary missions, Sentinel, Landsat 8/9, and commercial high-resolution platforms. Cloud computing platforms (Google Earth Engine, AWS, Microsoft Planetary Computer), unmanned aerial systems, and IoT devices have reshaped operational workflows. Persistent challenges, data accessibility, computational cost, domain shift, label scarcity, and model interpretability are identified, and priority directions are proposed, including self-supervised learning, physics-informed machine learning, multi-modal fusion, and explainable AI.

 
Keywords

Remote Sensing; Data Models; Deep Learning; Geospatial Foundation Models; Change Detection; Time-series analysis; Google Earth Engine; Sentinel; Landsat; GeoAI.

 
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