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Machine Learning at the Object: Fine-Grained Extraction and Analysis in Remote Sensing

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

Deadline for manuscript submissions: closed (20 August 2025) | Viewed by 4858

Special Issue Editors

Dr. Yongyang Xu

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: deep learning; vector data rendering, and processing; GIS applications; artificial intelligent applications in GIS and RS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhong Xie

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430000, China
Interests: image processing; 3-D rebuilding; spatial analysis; GIS; geo-computing; artificial intelligence; spatial cognition
Dr. Sheng Hu

E-Mail Website
Guest Editor
Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528225, China
Interests: GeoAI; urban data science and big data analytics; geospatial artificial intelligence; spatial analysis; spatial statistics; geoinformation; geospatial science; intelligent understanding of urban big data; urban functional area analysis
Dr. Anna Hu

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430000, China
Interests: remote-sensing image; CycleGAN (cycle generative adversarial networks); deep learning; information recovery; weakly supervised; road extraction; remote sensing image; generative adversarial networks

Special Issue Information

Dear Colleagues,

Benefiting from the continuous progress of remote sensing acquisition technology, aerial remote sensing technology has realized the ability to make all-day observations without interference from weather and other objective factors, making it possible to collect surface information for Earth observation in a rapid and high-quality manner. Accompanied by the global attention to Earth observation research and the rapid implementation of various Earth observation programs, many kinds of applications for interpretation based on remote sensing images have been developed and have progressed by leaps and bounds. Deep learning and computer vision algorithms provide the basis for the intelligent processing of visible remote sensing images.

Meanwhile, as one of the most important data sources for other research in fields such as urban 3D modeling and urban functional area classification, information on the locations of various land features has always been a research focus in optical remote sensing images. Research on intelligent interpretation based on optical remote sensing imaging has begun to focus more on the refinement and generalization of various types of research.

Given the above reasons, the interpretation of optical remote sensing images using computer vision and deep learning algorithms is currently a research focus in the field of remote sensing, and refined interpretation results have become an important data foundation for urban construction; the popular areas of research include the following:

  1. Remote sensing image object detection;
  2. Ground object extraction;
  3. Land type classification;
  4. Remote sensing image change detection;
  5. Urban functional area analysis using remote sensing images;
  6. Building pattern recognition;
  7. Remote sensing image cloud and fog removal;
  8. Deep learning techniques for enhanced land use and land cover classification;
  9. Time-series land use and land cover mapping;
  10. Building height extraction;
  11. Fusion of remote sensing image with multi-source data;
  12. Quantification of CO2 emissions from remote sensing images;
  13. Land surface temperature estimation.

Dr. Yongyang Xu
Prof. Dr. Zhong Xie
Dr. Sheng Hu
Dr. Anna Hu
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

  • deep learning
  • machine learning
  • remote sensing applications
  • classification
  • segmentation
  • remote sensing interpretation
  • pattern recognition
  • height extraction
  • object detection
  • land use and land cover
  • vision transformer model
  • multi-source remote sensing data

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Published Papers (4 papers)

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Research

29 pages, 6246 KiB  
Article
DASeg: A Domain-Adaptive Segmentation Pipeline Using Vision Foundation Models—Earthquake Damage Detection Use Case
by Huili Huang, Andrew Zhang, Danrong Zhang, Max Mahdi Roozbahani and James David Frost
Remote Sens. 2025, 17(16), 2812; https://doi.org/10.3390/rs17162812 - 14 Aug 2025
Viewed by 383
Abstract
Limited labeled imagery and tight response windows hinder the accurate damage quantification for post-disaster assessment. The objective of this study is to develop and evaluate a deep learning-based Domain-Adaptive Segmentation (DASeg) workflow to detect post-disaster damage using limited information [...] Read more.
Limited labeled imagery and tight response windows hinder the accurate damage quantification for post-disaster assessment. The objective of this study is to develop and evaluate a deep learning-based Domain-Adaptive Segmentation (DASeg) workflow to detect post-disaster damage using limited information available shortly after an event. DASeg unifies three Vision Foundation Models in an automatic workflow: fine-tuned DINOv2 supplies attention-based point prompts, fine-tuned Grounding DINO yields open-set box prompts, and a frozen Segment Anything Model (SAM) generates the final masks. In the earthquake-focused case study DASeg-Quake, the pipeline boosts mean Intersection over Union (mIoU) by 9.52% over prior work and 2.10% over state-of-the-art supervised baselines. In a zero-shot setting scenario, DASeg-Quake achieves the mIoU of 75.03% for geo-damage analysis, closely matching expert-level annotations. These results show that DASeg achieves superior workflow enhancement in infrastructure damage segmentation without needing pixel-level annotation, providing a practical solution for early-stage disaster response. Full article
(This article belongs to the Special Issue Machine Learning at the Object: Fine-Grained Extraction and Analysis in Remote Sensing)
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23 pages, 6199 KiB  
Article
PDAA: An End-to-End Polygon Dynamic Adjustment Algorithm for Building Footprint Extraction
by Longjie Luo, Jiangchen Cai, Bin Feng and Liufeng Tao
Remote Sens. 2025, 17(14), 2495; https://doi.org/10.3390/rs17142495 - 17 Jul 2025
Viewed by 341
Abstract
Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper [...] Read more.
Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article
(This article belongs to the Special Issue Machine Learning at the Object: Fine-Grained Extraction and Analysis in Remote Sensing)
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22 pages, 57199 KiB  
Article
CM-UNet++: A Multi-Level Information Optimized Network for Urban Water Body Extraction from High-Resolution Remote Sensing Imagery
by Jiangchen Cai, Liufeng Tao and Yang Li
Remote Sens. 2025, 17(6), 980; https://doi.org/10.3390/rs17060980 - 11 Mar 2025
Cited by 1 | Viewed by 1224
Abstract
Urban water bodies are crucial in urban planning and flood detection, and they are susceptible to changes due to climate change and rapid urbanization. With the development of high-resolution remote sensing technology and the success of semantic segmentation using deep learning in computer [...] Read more.
Urban water bodies are crucial in urban planning and flood detection, and they are susceptible to changes due to climate change and rapid urbanization. With the development of high-resolution remote sensing technology and the success of semantic segmentation using deep learning in computer vision, it is possible to extract urban water bodies from high-resolution remote sensing images. However, many urban water bodies are small, oddly shaped, silted, or spectrally similar to other objects, making their extraction extremely challenging. In this paper, we propose a neural network named CM-UNet++, a combination of the dense-skip module based on UNet++ and the CSMamba module to encode different levels’ information with interactions and then extract global and local information at each level. We use a size-weighted auxiliary loss function to balance feature maps of different levels. Additionally, features beyond RGB are incorporated into the input of the neural network to enhance the distinction between water bodies and other objects. We produced a labeled urban water extraction dataset, and experiments on this dataset show that CM-UNet++ attains 0.8781 on the IOU (intersection over union) metric, which indicates that this method outperforms other recent semantic segmentation methods and achieves better completeness, connectivity, and boundary accuracy. The proposed dense-skip module and CSMamba module significantly improve the extraction of small and spectrally indistinct water bodies. Furthermore, experiments on a public dataset confirm the method’s robustness. Full article
(This article belongs to the Special Issue Machine Learning at the Object: Fine-Grained Extraction and Analysis in Remote Sensing)
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24 pages, 16483 KiB  
Article
Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation
by Zhanlong Chen, Rui Wang and Yongyang Xu
Remote Sens. 2024, 16(18), 3424; https://doi.org/10.3390/rs16183424 - 14 Sep 2024
Cited by 2 | Viewed by 1966
Abstract
The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change detection (SSCD) methods have effectively reduced the reliance on labeled data. [...] Read more.
The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change detection (SSCD) methods have effectively reduced the reliance on labeled data. However, these methods primarily focus on utilizing unlabeled data through various training strategies, neglecting the impact of pseudo-changes and learning bias in models. When dealing with limited labeled data, abundant low-quality pseudo-labels generated by poorly performing models can hinder effective performance improvement, leading to the incomplete recognition results of changes to buildings. To address this issue, we propose a feature multi-scale information interaction and complementation semi-supervised method based on consistency regularization (MSFG-SemiCD), which includes a multi-scale feature fusion-guided change detection network (MSFGNet) and a semi-supervised update method. Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. Moreover, this enables the fusion and complementation of information between features, resulting in more complete change features. The semi-supervised update method employs a weak-to-strong consistency framework to achieve model parameter updates while maintaining perturbation invariance of unlabeled data at both input and encoder output features. Experimental results on the WHU-CD and LEVIR-CD datasets confirm the efficacy of the proposed method. There is a notable improvement in performance at both the 1% and 5% levels. The IOU in the WHU-CD dataset increased by 5.72% and 6.84%, respectively, while in the LEVIR-CD dataset, it improved by 18.44% and 5.52%, respectively. Full article
(This article belongs to the Special Issue Machine Learning at the Object: Fine-Grained Extraction and Analysis in Remote Sensing)
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