Remote Sensed Image Processing and Geospatial Intelligence

Dear Colleagues,

Remote sensing has become an indispensable tool for advancing our understanding of the Earth’s surface and the complex interactions between human and natural systems. With the rapid development of satellite, airborne, and drone-based platforms, unprecedented volumes of high-resolution spatial, spectral, and temporal data are now available. Effectively processing and interpreting this vast information requires advanced computational frameworks capable of extracting meaningful patterns and supporting data-driven decision-making in areas such as sustainable development, natural resource management, and environmental monitoring.

Within this context, GeoAI (Geographic Artificial Intelligence) has emerged as a transformative paradigm. By integrating artificial intelligence, machine learning, computer vision, and high-performance computing with remote sensing science, GeoAI enables robust image processing, feature extraction, land-cover classification, change detection, and predictive modeling from large-scale Earth observation datasets. This synergy is reshaping Geographic Information Science (GIS) and Earth Observation, ushering in a new era of intelligent geospatial analysis.

The impact of GeoAI is particularly evident in remote sensing applications. By combining advanced image processing techniques with deep learning architectures and innovative data mining approaches, GeoAI strengthens our ability to monitor agricultural productivity, guide urban and regional planning, assess natural hazards, and track environmental change. It enhances the precision, scalability, and timeliness of geospatial intelligence, offering unprecedented opportunities to address pressing global challenges and to better manage dynamic landscapes under conditions of rapid environmental and societal change.

This Special Issue aims to provide cutting-edge developments in remote sensing and GeoAI, with particular emphasis on computational methods that enhance our ability to observe, analyze, and forecast. Topics may span a wide range of topics, including but not limited to, land cover classification, change detection, spatiotemporal modeling, and predictive analytics. Studies that integrate multisource remote sensing data (e.g., hyperspectral, LiDAR, SAR), employ advanced machine learning and deep learning frameworks, or address issues of environmental sustainability, resilience, and coupled human–environment systems are especially encouraged.

Articles may address, but are not limited, to the following topics:

• Artificial Intelligence for land observation and monitoring.
• Geospatial Artificial Intelligence (GeoAI) for Remote Sensing applications.
• AI in geostatistics and spatiotemporal simulation for Remote Sensing.
• AI for Remote Sensing data acquisition, integration, analysis, planning, and prediction.
• Hyperspectral imagery (HSI), LiDAR, and Synthetic Aperture Radar (SAR).
• Image denoising, super-resolution, and high-resolution enhancement.
• Object detection, semantic segmentation, and feature extraction.
• Multimodal data fusion and spatial–temporal integration.
• Land change detection and monitoring.
• Natural resource management and environmental assessment.
• Land classification, monitoring, and forecasting.
• Deep learning for geospatial image processing.
• Contrastive learning, representation learning, and reinforcement learning.
• Meta-learning, transfer learning, and few-shot learning for geospatial tasks.
• Time-series prediction and forecasting from remote sensing data.
• Semantic reasoning, knowledge representation, and geospatial knowledge bases.
• Visual- and spatial-based perception enhancement and reasoning.
• Visual question answering (VQA) and visual reasoning for geospatial applications.
• Large Language Models (LLMs), Generative AI, and their integration with remote sensing.
• Natural disaster detection, monitoring, and forecasting.
• Applications for resilience and sustainability in coupled human–environment systems.

Dr. Lirong Yin
Dr. Kenan Li
Dr. Shan Liu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 in remote sensing and earth observation
• remote sensing data and satellite imagery optimization, enhancement, and mining
• environmental and resource remote sensing
• disaster and climate applications
• remote sensing image processing advances
• machine learning and deep learning
• semantic segmentation and object detection in satellite imagery
• hyperspectral imagery (HSI), LiDAR, and synthetic aperture radar (SAR)
• remote sensing for land, hydrology, and atmospheric sciences