GeoAI for Land Use Observations, Analysis and Forecasting

Dear Colleagues,

GeoAI, or geographic artificial intelligence, has emerged as a powerful tool for the observation, analysis, and forecasting of land use patterns that combines innovative artificial intelligence methods from space science, machine learning, deep learning, data mining, and high-performance computing to extract knowledge from spatial and temporal big data. GeoAI plays an important role in pushing geographic information science (GIS) and Earth observation toward a new stage of development by enhancing traditional geospatial analysis and mapping, as well as by changing the way we understand and manage complex human natural systems. When combined with remote sensing data, GeoAI can classify and map land cover, identify changes in land use, and predict future trends. In short, it has revolutionized the way we approach agricultural production, environmental protection, urban planning, and natural resource management.

Deep learning has transformed our understanding and utilization of both time and space. Through advanced neural network architectures, we can now extract meaningful patterns and representations from temporal and spatial data. By using remote sensing data and deep learning algorithms, it enables the classification and monitoring of land cover, aids in urban planning and resource management, supports decision-making in agriculture for increased productivity and food security, contributes to environmental protection and natural resource management, and provides decision support for sustainable land use policies.

Overall, GeoAI has the potential to foster far-reaching changes to our understanding of land use patterns and their impact on the environment and, as this technology continues to evolve, we can expect to see increasingly sophisticated applications. GeoAI has enormous potential to contribute to a more sustainable future.

In this Special Issue, we seek groundbreaking research and case studies that demonstrate future applications and advances in geographic artificial intelligence. Relevant topics include, but are not limited to the following:

• Artificial intelligence for land use;
• Geospatial artificial intelligence (geospatial AI or GeoAI) for land use;
• AI in geostatistics and spatiotemporal simulation;
• AI For geospatial data acquisition, analysis, planning, and prediction;
• Hyperspectral imaging (HSI), lidar, and synthetic aperture radar (SAR);
• Land change detection;
• Natural resource management;
• Semantic reasoning, semantic representation, and knowledge bases;
• Object detection and semantic segmentation;
• Natural disaster forecasting;
• Contrastive learning, representation learning, and reinforcement learning;
• Meta-learning, transfer learning, and few-shot learning;
• Spatiotemporal integration;
• Time series prediction and forecasting;
• Multimodal artificial intelligence;
• Visual- and spatial-based perception enhancement and reasoning;
• Image denoising and high-resolution;
• Visual question answer (VQA) and visual reasoning;
• Large language model (LLM) and GenAI;
• Wetland classification and forecasting

Dr. Wenfeng Zheng
Dr. Kenan Li
Prof. Dr. Xuan 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. Land 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 2600 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.

• remote sensing
• land use and land cover
• GeoAI
• AI and machine learning