GeoAI-Driven Remote Sensing for Smart Environmental and Resource Monitoring

Dear Colleagues,

The rapid development of Earth observation technologies has led to an unprecedented increase in the availability of multisource, multiscale, and multimodal geospatial data. Satellite imagery, LiDAR point clouds, unmanned aerial vehicle (UAV) observations, and ground-based sensor networks are continuously generating massive amounts of spatial information, providing new opportunities for understanding environmental processes and supporting sustainable resource management. Meanwhile, recent advances in Geographic Artificial Intelligence (GeoAI), including deep learning, foundation models, graph neural networks, and large language models, have significantly improved the ability to extract knowledge from complex remote sensing data and enable intelligent geospatial analysis.

GeoAI-driven remote sensing is transforming the way environmental systems and natural resources are monitored, modeled, and managed. These emerging technologies facilitate more accurate and efficient solutions for land cover mapping, change detection, ecosystem assessment, disaster monitoring, climate change analysis, and digital twin construction. In addition, the increasing integration of multisource observations and spatial-temporal data analytics provides new possibilities for smart environmental governance and sustainable development.

The purpose of this Special Issue is to present recent advances in theories, methods, and applications of GeoAI-enabled remote sensing for smart environmental and resource monitoring. Contributions addressing innovative algorithms, data fusion techniques, and practical applications across multiple spatial and temporal scales are particularly welcome. Original research articles, review papers, and case studies are encouraged. Topics include, but are not limited to, the following:

• Foundation models and large geospatial models for earth observation;
• Multimodal GeoAI and data fusion for environmental intelligence;
• Physics-informed and hybrid AI for remote sensing;
• Spatiotemporal deep learning for environmental monitoring and forecasting;
• Climate, atmospheric, and air quality monitoring using GeoAI;
• Disaster monitoring with remote sensing;
• Smart water and ecosystem resource monitoring;
• Precision agriculture and sustainable land management;
• Operational GeoAI systems and digital twins for environmental decision support.

The primary aim of this Special Issue is to consolidate and advance the state-of-the-art in GeoAI-driven remote sensing for smart environmental and resource monitoring, bridging the gap between cutting-edge artificial intelligence research and operational Earth observation applications, by bringing together innovative contributions that push the boundaries of GeoAI methodologies while demonstrating their practical utility in real-world environmental monitoring and resource management contexts, ultimately transforming massive, multidimensional, and multisource raw remote sensing data into actionable environmental intelligence capable of supporting real-time decision-making.

This Special Issue is strategically aligned with the core scope and mission of Remote Sensing by focusing on the intersection of artificial intelligence and Earth observation, which represents one of the most significant and transformative developments in the field, fundamentally reshaping how we extract information from remotely sensed data and translate it into environmental understanding, while spanning the journal's key pillars including sensor technologies and data acquisition, data processing and information extraction, environmental and resource applications, methodological innovation, multidisciplinary integration, and the promotion of open science, thereby capturing the cutting edge of remote sensing research and providing a vital resource for researchers, practitioners, and policymakers seeking to leverage GeoAI for smarter environmental and resource monitoring in an era of unprecedented environmental change and data availability.

Dr. Lizhi Miao
Dr. Qunying Huang
Dr. Lei Huang
Dr. Daoye Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• GeoAI
• remote sensing
• earth observation
• environmental monitoring
• natural resource management
• deep learning
• spatial-temporal big data
• multimodal data fusion