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GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition)

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2632

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Special Issue Editors

Dr. Min Huang

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Guest Editor

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

Prof. Dr. Hui Lin

E-Mail Website
Guest Editor

School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
Interests: remote sensing; digital twin; spatial information technology in humanities and social sciences

Prof. Dr. Nengcheng Chen

E-Mail Website
Guest Editor

National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430074, China
Interests: digital twin; earth observation sensor network; spatiotemporal big data intelligence; geosimulation decision; smart city and smart watershed
Special Issues, Collections and Topics in MDPI journals

Dr. Daoye Zhu

E-Mail Website
Guest Editor

Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
Interests: remote sensing; geospatial artificial intelligence; spatial data science; spatial analysis and modeling; spatial information integration
Special Issues, Collections and Topics in MDPI journals

Dr. Kaiqi Chen

E-Mail Website
Guest Editor Assistant

School of Geosciences and Info-Physics, Central South University (CSU), Changsha 410083, China
Interests: spatiotemporal prediction; GeoAI; spatial analysis and modeling

Special Issue Information

Dear Colleagues,

With the extensive development of earth observation (EO) technologies (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) in recent years, EO data have accumulated quickly to the petabyte-level, which provides the greatest opportunities yet for earth environmental science, though they also pose the grandest challenges for the processing of these EO big data. Owing to the development and advancement of artificial intelligence (AI), especially Geospatial AI (GeoAI) methods and techniques (e.g., spatiotemporal machine learning and deep learning), the modeling, processing, and analysis of EO big data have arrived at a new paradigm. By integrating EO big data and GeoAI methods, more comprehensive and in-depth investigations into earth environmental science have become possible.

This Special Issue invites the submission of methodological or applied studies using GeoAI and EO big data for investigating matter, energy, and information in the hydrosphere, lithosphere, biosphere, and atmosphere on the surface of the Earth. The scale can be local, regional, or global, but large-scale and long time-series studies will be preferred. In addition, monitoring and analysis studies of the key thematic indicators for high-impact events or disasters such as droughts, floods, earthquakes, tsunamis, and volcanic eruptions are especially welcome.

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

Analysis and mining of EO (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) big data;
Novel GeoAI models and frameworks (e.g., spatiotemporal machine learning/deep learning) for modeling/processing/analyzing EO big data;
Retrievals of environmental variables (e.g., precipitation, land/sea surface temperature, soil moisture, aerosols, vegetation index, sea ice concentration, sea surface salinity, snow cover, chlorophyll-a concentration);
Environmental variables’ monitoring and prediction;
Postprocessing of environmental variable retrievals (e.g., multi-source data fusion, downscaling, and image restoration);
Extracting information from EO big data (e.g., classification, segmentation, target detection, dynamic monitoring, and prediction);
Natural hazards’ (e.g., drought, flood, waterlogging, wildfire, landslide, surge earthquake, tsunami, and volcanic eruption) monitoring and evaluation;
Crop yield estimation;
Land cover land use mapping and scenario prediction;
Monitoring and analysis of high-impact events (e.g., epidemic outbreaks, oil spills, gas pipeline ruptures, carbon neutrality, and emission peak).

Dr. Min Huang
Prof. Dr. Hui Lin
Prof. Dr. Nengcheng Chen
Dr. Daoye Zhu
Guest Editors

Dr. Kaiqi Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

earth observation big data
GeoAI
multisource/multimodal data fusion
long time-series analysis
retrievals of environmental variables
postprocessing of environmental variable retrievals
monitoring, evaluation, and prediction
land cover land use
natural hazards
high-impact events

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Related Special Issue

GeoAI and EO Big Data Driven Advances in Earth Environmental Science in Remote Sensing (25 articles)

Published Papers (5 papers)

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Research

28 pages, 15164 KB

Open AccessArticle

Fusion and Analysis of Multi-Source Precipitation Data (2003–2021) in the Yangtze River Basin

by Runzhi Sun, Yanbo Zhang, Jinglin Cong, Gang Chen and Jifa Chen

Remote Sens. 2026, 18(8), 1191; https://doi.org/10.3390/rs18081191 - 16 Apr 2026

Abstract

A vital region for China’s water resource storage and ecological balance maintenance, the Yangtze River Basin is strategically significant for maintaining regional water security and promoting long-term social and economic development. Precipitation is the main driver of the hydrological cycle. In order to address current problems with the basin’s ecological environment and water supplies, comprehensive analyses of multi-source precipitation data are necessary. They provide an essential scientific basis for evaluating the sustainability of water resources in the Yangtze River Basin in the context of climate change. Most existing precipitation fusion studies utilize only a limited number of datasets and do not fully consider the independence among different data sources, which leads to less-than-ideal fusion accuracy and assessment metrics. This paper employs the Triple Collocation (TC) method to evaluate and fuse multiple precipitation datasets over a 19-year period from 2003 to 2021, with the aim of enhancing precipitation accuracy in the Yangtze River Basin. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation data were found to have the highest accuracy among seven datasets, with a Correlation Coefficient (CC), Relative Bias (Rbias), and Root Mean Square Error (RMSE) of 0.907, −0.027, and 25.930 mm, respectively. The “MSWEP–PERSIANN–NOAH (MPN)” fusion was shown to be the best using the Multiplicative Triple Collocation (MTC) method in conjunction with cross-error analysis. Compared to MSWEP alone, it improved CC by 0.8% and decreased RMSE by 3.8%, with matching spatial-grid CC and RMSE improvements of 1.2% and 1.8%, respectively. Further spatiotemporal analysis of the fused data increase detection capabilities for short-term flood and waterlogging occurrences and provide better knowledge of basin water-resource status. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition))

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22 pages, 6238 KB

Open AccessArticle

Fusion-Based Regional ZTD Modeling Using ERA5 and GNSS via Residual Correction Kriging

by Yang Cai, Hongyang Ma, Zhiliang Wang, Shuaishuai Jia, Xin Duan, Ge Shi and Chuang Chen

Remote Sens. 2026, 18(6), 963; https://doi.org/10.3390/rs18060963 - 23 Mar 2026

Viewed by 344

Abstract

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition))

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23 pages, 6853 KB

Open AccessArticle

IDD-DETR: Lightweight Multi-Defect Detection Model for Transmission Line Insulators Based on UAV Remote Sensing

by Cheng Xu, Xin Liu, Jiaxin Wang, Yun Ding and Chunhou Zheng

Remote Sens. 2026, 18(3), 486; https://doi.org/10.3390/rs18030486 - 3 Feb 2026

Viewed by 558

Abstract

Aiming to address the challenges of excessive model parameters, high computational complexity, strong complex background interference, and inadequate small-target detection found in insulator defect detection when using UAV remote sensing imagery of transmission lines, we propose a lightweight multi-defect detection model—Insulator Defect Detection-DETR (IDD-DETR). Specifically, we introduce a lightweight multi-starblock feature extractor (LMS-FE) as the backbone network to enhance its feature extraction capacity. Next, in order to enhance small-defect detection performance, a multi-scale feature pyramid (SOEP) is constructed by integrating shallow high-resolution features into the neck network. Additionally, a lightweight multi-branch feature fusion module (LMB-FF) is designed to efficiently fuse spatial and semantic information of small defects, suppressing background interference while optimizing model complexity. Finally, experimental results demonstrate that IDD-DETR achieves a 2.2% improvement in mean average precision (mAP) on the insulator small-defect dataset compared with the baseline algorithm, with model parameters and computation reduced by 44.9% and 47.1%, respectively. It also reaches a detection speed of 61.2 frames per second, satisfying the lightweight and high-precision requirements for edge deployment in transmission line inspection scenarios. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition))

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22 pages, 5960 KB

Open AccessArticle

JFDet: Joint Fusion and Detection for Multimodal Remote Sensing Imagery

by Wenhao Xu and You Yang

Remote Sens. 2026, 18(1), 176; https://doi.org/10.3390/rs18010176 - 5 Jan 2026

Viewed by 688

Abstract

Multimodal remote sensing imagery, such as visible and infrared data, offers crucial complementary information that is vital for time-sensitive emergency applications like search and rescue or disaster monitoring, where robust detection under adverse conditions is essential. However, existing methods’ object detection performance is often suboptimal due to task-independent fusion and inherent modality inconsistency. To address this issue, we propose a joint fusion and detection approach for multimodal remote sensing imagery (JFDet). First, a gradient-enhanced residual module (GERM) is introduced to combine dense feature connections with gradient residual pathways, effectively enhancing structural representation and fine-grained texture details in fused images. For robust detection, we introduce a second-order channel attention (SOCA) mechanism and design a multi-scale contextual feature-encoding (MCFE) module to capture higher-order semantic dependencies, enrich multi-scale contextual information, and thereby improve the recognition of small and variably scaled objects. Furthermore, a dual-loss feedback strategy propagates detection loss to the fusion network, enabling adaptive synergy between low-level fusion and high-level detection. Experiments on the VEDAI and FLIR-ADAS datasets demonstrate that the proposed detection-driven fusion framework significantly improves both fusion quality and detection accuracy compared with state-of-the-art methods, highlighting its effectiveness and high potential for mission-critical multimodal remote sensing and time-sensitive application. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition))

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29 pages, 21548 KB

Open AccessArticle

MSCANet: Multi-Scale Spatial-Channel Attention Network for Urbanization Intelligent Monitoring

by Zhande Dong, Daoye Zhu, Min Huang, Qifeng Lin, Lasse Møller-Jensen and Elisabete A. Silva

Remote Sens. 2026, 18(1), 159; https://doi.org/10.3390/rs18010159 - 3 Jan 2026

Cited by 1 | Viewed by 542

Abstract

Rapid urbanization drives economic growth but also brings complex environmental and social issues, highlighting the urgent need for efficient urbanization monitoring techniques. However, datasets for urbanization monitoring are often lacking in rapidly developing urban areas. At the methodological level, Convolutional Neural Networks (CNNs) and Transformer-based models for urbanization monitoring exhibit limitations in balancing computational efficiency and global modeling. The recently emerging parallel large kernel convolutional networks partially alleviate the conflict between global modeling and computational efficiency, but they employ simple element-wise addition to fuse multi-scale features. This crude mechanism struggles to fully leverage multi-scale information. To address this, this paper takes Accra, the capital of Ghana, as a case study and proposes an urbanization monitoring framework covering both dataset construction and model design. Methodologically, we propose the Multi-Scale Spatial-Channel Attention Network (MSCANet). Its core component, the Multi-Scale Spatial-Channel Attention Module (MSCAM), jointly models spatial and channel dimensions to mitigate the common confusion problem in parallel large kernel convolutional architectures. Furthermore, we adaptively modified the MSCAM to propose the Multi-Scale Spatial-Channel Attention Feature Fusion Module (MSCA-FFM) module for effectively integrating multi-modal information during the fusion stage. Experimental results show that MSCANet achieves optimal performance on the self-built Accra dataset, with a mean intersection over union (mIoU) of 95.02%, an overall accuracy (OA) of 98.70%, and a mean F1 Score (mF1) of 97.43%. To further validate the model’s generalization capability, supplementary experiments were conducted on the public ISPRS Potsdam dataset. The results demonstrate that the MSCANet series of models remain competitive, achieving an overall mIoU of 80.92%, with particularly strong performance in the “Building” (mIoU 92.26%) and “Impervious surface” (mIoU 84.63%) categories. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science (Second Edition))

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