Intelligent Vision Systems for Remote Sensing and Environmental Monitoring

Dear Colleagues,

This Special Issue aims to explore the transformative role of artificial intelligence (AI) in advancing remote sensing image processing for environmental monitoring across theoretical, methodological, and applied domains. Rapid advancements in AI, particularly in deep learning, computer vision, and multimodal fusion, are revolutionizing how we interpret Earth observation data, enabling unprecedented accuracy, scalability, and automation in extracting actionable environmental intelligence. This Special Issue seeks to showcase cutting-edge research addressing domain-specific challenges—such as processing high-dimensional data, ensuring model robustness across diverse geographies, and achieving computational efficiency for near-real-time analysis.

The scope encompasses three core themes. The Theory section focuses on foundational AI frameworks tailored for geospatial data, including novel architectures (e.g., vision transformers, graph neural networks, and hybrid CNN-transformers), self-supervised learning for label-scarce environments, and theoretical insights into model generalizability across different sensors and temporal scales. The Methods section emphasizes algorithmic innovations for the remote sensing pipeline, such as lightweight models for onboard satellite and drone processing, federated learning for distributed data analysis, advanced techniques for fusing optical, SAR, LiDAR, and hyperspectral data, and robust change detection algorithms. The Applications section highlights AI-driven breakthroughs in critical domains such as climate change monitoring, precision agriculture, disaster response, biodiversity conservation, and water resource management (water quality assessment, watershed monitoring).

This Special Issue encourages interdisciplinary contributions bridging AI, computer vision, and environmental science. Researchers are invited to submit original articles, reviews, and case studies. Research areas may include (but are not limited to) the following:

1. Image enhancement and atmospheric correction;
2. Semantic segmentation and land cover classification;
3. Object detection;
4. Change detection and time-series analysis;
5. Image super-resolution and pansharpening;
6. Anomaly detection for environmental threats;
7. Three-dimensional reconstruction from remote sensing data;
8. Image fusion.

Dr. Junchao Zhang
Guest Editor

Dr. Zhengliang Zhu
Guest Editor Assistant

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. Technologies is an international peer-reviewed open access monthly 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 1600 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.

• image processing
• artifical intelligent
• remote sensing
• environmental monitoring
• object detection
• 3D reconstruction