Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks
The analysis of aerial and satellite images for building footprint detection is one of the major challenges in photogrammetry and remote sensing. This information is useful for various applications, such as urban planning, disaster monitoring, and 3D city modeling. However, it has become a significa...
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MDPI AG
2025-05-01
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| author | Ehsan Haghighi Gashti Hanieh Bahiraei Mohammad Javad Valadan Zoej Ebrahim Ghaderpour |
| author_facet | Ehsan Haghighi Gashti Hanieh Bahiraei Mohammad Javad Valadan Zoej Ebrahim Ghaderpour |
| author_sort | Ehsan Haghighi Gashti |
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| description | The analysis of aerial and satellite images for building footprint detection is one of the major challenges in photogrammetry and remote sensing. This information is useful for various applications, such as urban planning, disaster monitoring, and 3D city modeling. However, it has become a significant challenge due to the diverse characteristics of buildings, such as shape, size, and shadow interference. This study investigated the simultaneous use of aerial and satellite images to improve the accuracy of deep learning models in building footprint detection. For this purpose, aerial images with a spatial resolution of 30 cm and Sentinel-2 satellite imagery were employed. Several satellite-derived spectral indices were extracted from the Sentinel-2 image. Then, U-Net models combined with ResNet-18 and ResNet-34 were trained on these data. The results showed that the combination of the U-Net model with ResNet-34, trained on a dataset obtained by integrating aerial images and satellite indices, referred to as RGB–Sentinel–ResNet34, achieved the best performance among the evaluated models. This model attained an accuracy of 96.99%, an F1-score of 90.57%, and an Intersection over Union of 73.86%. Compared to other models, RGB–Sentinel–ResNet34 showed a significant improvement in accuracy and generalization capability. The findings indicated that the simultaneous use of aerial and satellite data can substantially enhance the accuracy of building footprint detection. |
| format | Article |
| id | doaj-art-fb302e2331be4da3a7dbb42d4408c8e6 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-fb302e2331be4da3a7dbb42d4408c8e62025-08-20T01:56:28ZengMDPI AGInformation2078-24892025-05-0116538010.3390/info16050380Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural NetworksEhsan Haghighi Gashti0Hanieh Bahiraei1Mohammad Javad Valadan Zoej2Ebrahim Ghaderpour3School of Surveying and Geospatial Eng, College of Engineering, University of Tehran, 1417935840 Tehran, IranFaculty of Geomatics Engineering, K. N. Toosi University of Technology, 1969764499 Tehran, IranFaculty of Geomatics Engineering, K. N. Toosi University of Technology, 1969764499 Tehran, IranDepartment of Earth Sciences, Sapienza University of Rome, P. le Aldo Moro, 5, 00185 Rome, ItalyThe analysis of aerial and satellite images for building footprint detection is one of the major challenges in photogrammetry and remote sensing. This information is useful for various applications, such as urban planning, disaster monitoring, and 3D city modeling. However, it has become a significant challenge due to the diverse characteristics of buildings, such as shape, size, and shadow interference. This study investigated the simultaneous use of aerial and satellite images to improve the accuracy of deep learning models in building footprint detection. For this purpose, aerial images with a spatial resolution of 30 cm and Sentinel-2 satellite imagery were employed. Several satellite-derived spectral indices were extracted from the Sentinel-2 image. Then, U-Net models combined with ResNet-18 and ResNet-34 were trained on these data. The results showed that the combination of the U-Net model with ResNet-34, trained on a dataset obtained by integrating aerial images and satellite indices, referred to as RGB–Sentinel–ResNet34, achieved the best performance among the evaluated models. This model attained an accuracy of 96.99%, an F1-score of 90.57%, and an Intersection over Union of 73.86%. Compared to other models, RGB–Sentinel–ResNet34 showed a significant improvement in accuracy and generalization capability. The findings indicated that the simultaneous use of aerial and satellite data can substantially enhance the accuracy of building footprint detection.https://www.mdpi.com/2078-2489/16/5/380deep learningsemantic segmentationU-NetResNetSentinel-2 |
| spellingShingle | Ehsan Haghighi Gashti Hanieh Bahiraei Mohammad Javad Valadan Zoej Ebrahim Ghaderpour Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks Information deep learning semantic segmentation U-Net ResNet Sentinel-2 |
| title | Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks |
| title_full | Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks |
| title_fullStr | Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks |
| title_full_unstemmed | Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks |
| title_short | Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks |
| title_sort | fusion of aerial and satellite images for automatic extraction of building footprint information using deep neural networks |
| topic | deep learning semantic segmentation U-Net ResNet Sentinel-2 |
| url | https://www.mdpi.com/2078-2489/16/5/380 |
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