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Environment Observation Analysis Based on Remote Sensing and Geospatial Artificial Intelligence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 426

Special Issue Editors


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Guest Editor
National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing; forecasting; artificial intelligence; spatiotemporal data mining; uncertainty analysis

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Guest Editor
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Interests: hydrologic modeling; data assimilation; soil moisture remote sensing and machine learning
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430078, China
Interests: flood monitoring; geospatial analysis; smart city; sensor web
Special Issues, Collections and Topics in MDPI journals
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
Interests: remote sensing; machine learning; classification; land use land cover; time-series analysis; urban informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite remote sensing provides a direct method for the global observation of environmental concerns, such as land-use/land-cover changes, soil moisture, precipitation, floods, or surface water dynamics. The utilization of remote sensing big data to make environmental observations could prove vital to disaster management, information analysis, and risk preparations. With the development of geospatial artificial intelligence, the ability to handle hundreds of or thousands of satellite images has been greatly improved and the large-scale urban, hydrological, meteorological, and ecological retrieval of land-surface parameters is possible. Therefore, it would be interesting to use satellite remote sensing and AI approaches to reveal large-scale geographical phenomena and laws in order to better understand the hydrosphere, lithosphere, biosphere, atmosphere, and anthroposphere.
Anthropogenic activities have been changing the Earth’s environment, and it is necessary to monitor the incoming changes on a regular basis. We invite your insights and contributions in various research areas involving remote sensing combined with a GeoAI approach. Papers can focus on, but are not limited to, the following topics:

  • Monitoring of floods, droughts, and other disasters on large spatial scales;
  • Spatiotemporal monitoring and prediction of spatiotemporal processes;
  • Urban, hydrological, meteorological, and ecological retrieval of land-surface parameters;
  • Fine-scale monitoring of anthropogenic activities and their impacts;
  • Novel GeoAI approaches for environmental analysis and prediction;
  • Multisource data fusion using GeoAI approaches;
  • Global environmental risk analysis using big data;
  • Data–knowledge fusion for environmental remote sensing.

Prof. Dr. Lei Xu
Dr. Peyman Abbaszadeh
Dr. Wenying Du
Dr. Min Huang
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.

Keywords

  • remote sensing
  • big data
  • GeoAI
  • spatiotemporal processes
  • disaster analysis
  • large-scale monitoring and prediction

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Published Papers (1 paper)

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Research

20 pages, 4380 KiB  
Article
Advancing Built-Up Area Monitoring Through Multi-Temporal Satellite Data Fusion and Machine Learning-Based Geospatial Analysis
by Alessandro Vitale and Francesco Lamonaca
Remote Sens. 2025, 17(11), 1830; https://doi.org/10.3390/rs17111830 - 23 May 2025
Viewed by 46
Abstract
Monitoring built-up dynamics is essential for sustainable urban and territorial planning. This study presents an innovative geospatial methodology integrating multi-temporal satellite data fusion, transfer learning, machine learning classification, and open-access cloud computing to systematically identify, quantify, and map the spatiotemporal evolution of built-up [...] Read more.
Monitoring built-up dynamics is essential for sustainable urban and territorial planning. This study presents an innovative geospatial methodology integrating multi-temporal satellite data fusion, transfer learning, machine learning classification, and open-access cloud computing to systematically identify, quantify, and map the spatiotemporal evolution of built-up areas. The methodology was applied at a territorial scale in southern Italy using Landsat multispectral imagery acquired and elaborated through Google Earth Engine. Compared to more conventional classification methods, the proposed integrated approach ensures scalability, reproducibility, and computational efficiency. Landsat multispectral imagery from 2006 to 2024 was classified using a Random Forest (RF) algorithm, trained and validated with CORINE Land Cover maps for 2006, 2012, and 2018. For 2024, a transfer learning strategy was adopted, enabling classification through a model fine-tuned with historical data and validated independently. Accuracy assessment returned an Overall Accuracy (OA) of 0.890 and F1-scores between 0.803 and 0.811 for 2006–2018. For 2024, the OA reached 0.926 with an F1-score of 0.926, confirming the effectiveness of the proposed framework. This integrated methodology not only allows for determining the extent of urban expansion over the considered timelines, but, by introducing two spatial metrics, Urban Density and the Urban Dispersion Index (UDI), also enables the characterization of the morphological evolution of urban growth. The methodology ensures spatial and temporal consistency, offering a scalable and automated framework for long-term monitoring that provides a decision support tool for urban growth management and environmental planning, especially in data-limited contexts. Full article
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