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Geospatial Artificial Intelligence (AI) in Earth Observation, Remote Sensing and GIScience

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 2184

Special Issue Editors

School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: artificial intelligence; intelligent transportation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: geoInformatics; urban planning; urban renewal; real estate; GIS/RS; AI/ML; social equity; land development; urbanization; space value modelling; post-productivism transformation; social sensing; GeoAI; land management; land policy
Special Issues, Collections and Topics in MDPI journals
Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA
Interests: geoinformatics; spatial computation and modeling of community resilience/sustainability; data science and statistics in land use; geo-simulation of human and environmental systems; GeoAI (artificial intelligence) frameworks; integrated geo-cyber-infrastructures; urban planning; GIS/RS; AI/ML; social equity; land development; urbanization; space value modelling; social sensing; GeoAI; land management; land policy
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Resource and Environment Engineering, Guizhou University, Guiyang, China
Interests: AI/ML; complex dynamics; pattern recognition; visual reasoning; visual question answering; NLP; surgical robot; geospatial AI; GIS/RS; image fusion; surgical vision; 3D visualization; artificial neural network; computer graphics; image processing; machine vision; 3D reconstruction; medical imaging; data mining; earth surface process; cloud computing; geography and environmental science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial Artificial Intelligence (Geospatial AI) approaches have had a transformative influence on Earth observation and remote sensing fields such as nature language processing and computer vision. With the progression in deep learning algorithms, software and hardware technologies, scalable computation platforms, and the availability of high-resolution geospatial data are empowering the fast-growing field of Geospatial AI. These state-of-the-art methods have enabled a next generation of Earth observation and remote sensing and provided new means for researching Earth’s surface at a variety of scales from land use changes to other geographic forms and processes in general. Moreover, in the past few decades, to capture the dynamic process of space change, which is often driven by the combination of synergetic spatial and aspatial factors and their interactions, researchers have made great efforts to unravel the mechanism behind it. The great strides in complex analysis and Geospatial AI in spatial analysis have fundamentally changed traditional methodologies and have provided deeper theoretical insight into the dynamics of space change.

Geospatial AI emphasizes the cognition of the geographical environment and enriches GIScience by integrating multi-angle, multi-spectrum, multi-platform, multi-scale data. Meanwhile, although the multi-modal remote sensing data fusion can break through the limitation of single-modal data, eliminate redundancy, and achieve the effective combination and utilization of complementary information, multi-modal AI structures come at an enormous computational cost. Thus, simulating reality with data-driven machine learning within a relatively simple framework is desired. To a large extent, modern Geospatial AI systems do not only establish assumptions and structured concepts about the operating principles of the world but also tend to minimize the structure of algorithms to preserve the simplicity of the algorithm and explain complex scenes on Earth’s surface. Further studies require the combination of macroscopic geographic zoning deconstruction and microscopic visual cognition to develop complexity metrics and form multi-scale adaptive schemes. More practices are needed to reveal the application fields of different AI algorithms.

In this Special Issue, we will try to inspire the growth and distribution of open Geospatial AI tools that can be re-processed for GIScience research and education. Submissions demonstrating the added value of taking a Geospatial AI approach over existing approaches would be preferred. Papers should ideally also allow for insights into the mechanistic underpinnings of the system being investigated. New theories and methods of AI applications in spatially explicit AI models, spatial prediction and interpolation, earth observation, social sensing, and geospatial semantics are all welcomed in this Special Issue. Potential topics in this collection include, but are not limited to, the following:

  • Geospatial AI for object detection, localization, and classification.
  • Geospatial AI for agent-based modeling and cellular automata.
  • Geospatial AI for object segmentation, reconstruction, and registration.
  • Geospatial AI for anomaly/novelty detection and visual search.
  • Geospatial AI for using light detection and range (LiDAR) data.
  • Geospatial AI for developing early warning systems.
  • Geospatial AI for climate trace.
  • Geospatial AI for environmental watch projects such as biomass watch, fishing watch, forest watch, and beyond.
  • Geospatial AI for generating new geo-spatial datasets in Earth’s domain.
  • Geospatial AI for smart conveyance and autonomous cars.
  • Geospatial AI for all other earth observation applications.
  • Geospatial AI for modeling land use and land cover changes.
  • Geospatial AI for social sensing.
  • Geospatial AI for urban visual intelligence.
  • Geospatial AI for seismic analysis and prediction.
  • Geospatial AI for city understanding and analysis.
  • Geospatial AI for social intelligence.

Dr. Shan Liu
Prof. Dr. Xuan Liu
Dr. Kenan Li
Dr. Zhengtong Yin
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. Applied Sciences 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 2400 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.

Keywords

  • geospatial AI for object detection, localization, and classification
  • geospatial AI for agent-based modeling and cellular automata
  • geospatial AI for object segmentation, reconstruction, and registration
  • geospatial AI for anomaly/novelty detection and visual search
  • geospatial AI for using light detection and range (LiDAR) data
  • geospatial AI for developing early warning systems
  • geospatial AI for climate trace
  • geospatial AI for environmental watch projects such as biomass watch, fishing watch, forest watch and beyond
  • geospatial AI for generating new geo-spatial datasets in Earth’s domain
  • geospatial AI for smart conveyance and autonomous cars
  • geospatial AI for all other earth observation applications
  • geospatial AI for modeling land use and land cover changes
  • geospatial AI for social sensing
  • geospatial AI for urban visual intelligence
  • geospatial AI for seismic analysis and prediction
  • geospatial AI for city understanding and analysis
  • geospatial AI for social intelligence

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Published Papers (1 paper)

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Research

20 pages, 69470 KiB  
Article
Refined Land Use Classification for Urban Core Area from Remote Sensing Imagery by the EfficientNetV2 Model
by Zhenbao Wang, Yuqi Liang, Yanfang He, Yidan Cui and Xiaoxian Zhang
Appl. Sci. 2024, 14(16), 7235; https://doi.org/10.3390/app14167235 - 16 Aug 2024
Cited by 1 | Viewed by 1773
Abstract
In the context of accelerated urbanization, assessing the quality of the existing built environment plays a crucial role in urban renewal. In the existing research and use of deep learning models, most categories are urban construction areas, forest land, farmland, and other categories. [...] Read more.
In the context of accelerated urbanization, assessing the quality of the existing built environment plays a crucial role in urban renewal. In the existing research and use of deep learning models, most categories are urban construction areas, forest land, farmland, and other categories. These categories are not conducive to a more accurate analysis of the spatial distribution characteristics of urban green space, parking space, blue space, and square. A small sample of refined land use classification data for urban built-up areas was produced using remote sensing images. The large-scale remote sensing images were classified using deep learning models, with the objective of inferring the fine land category of each tile image. In this study, satellite remote sensing images of four cities, Handan, Shijiazhuang, Xingtai, and Tangshan, were acquired by Google Class 19 RGB three-channel satellite remote sensing images to establish a data set containing fourteen urban land use classifications. The convolutional neural network model EfficientNetV2 is used to train and validate the network framework that performs well on computer vision tasks and enables intelligent image classification of urban remote sensing images. The model classification effect is compared and analyzed through accuracy, precision, recall, and F1-score. The results show that the EfficientNetV2 model has a classification recognition accuracy of 84.56% on the constructed data set. The testing set accuracy increases sequentially after transfer learning. This paper verifies that the proposed research framework has good practicality and that the results of the land use classification are conducive to the fine-grained quantitative analysis of built-up environmental quality. Full article
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