Intelligent Technology of Buildings Plan Construction, Based on Aerial Photography of their Roofs
DOI:
https://doi.org/10.31649/1997-9266-2024-172-1-101-109Keywords:
intelligent technology, aerial photography, artificial intelligence, plan creation, roof identification, remote sensing data, image recognition, pseudo-masksAbstract
The article is devoted to the development of an intelligent technology for the construction of building plans based on the data of remote sensing of the Earth. Such data can be the data of aerial photographs or high-quality satellite images.
A detailed review of the types of roofs was carried out by analyzing the most common classifications, and the most typical of them were determined, on the example of which, the improvement of the intelligent technology of building plan construction based on aerial photography data can be carried out. Modern and traditional methods of image analysis that can be used to solve this problem are characterized. The methods are chosen, which are the most advanced and can be effective for this class of tasks.
Generalized algorithm for the class of single-pitched, gable, flat and hip roofs has been developed.
It is proposed to improve the intelligent technology of constructing a plan of buildings based on the data of aerial photography of their roofs, by integrating the DETR detection model (“DEtection TRansformer”) together with the segmentation based on ViTs (Vision Transformers) for a comprehensive solution to the problems of finding and identifying roofs, in a first approximation, for further improving the construction of building plans. Combined approach is proposed that takes advantage of the strengths of both models by using the DETR model to localize groups of roofs in large-scale images, then using ViTs to accurately segment similar types of roofs.
Comparison of the accuracy of models for image segmentation and object detection in images was made. The results of the approbation of the improved technology of construction of the plan of buildings based on the data of aerial photography of their roofs are characterized: the algorithm, approaches and software on the test data of aerial photography of the public dataset, which proved their effectiveness. Possible improvements of the proposed technology due to the use of pseudo masks are suggested.
The results of the work can be extended to other types of building roofs, on the condition of proper adaptation in accordance with the characteristic features of specific types of roofs.
References
Є. М. Крижановський, В. Б. Мокін, А. Р. Ящолт, і Л. М. Скорина, Системний аналіз та проектування ГІС, електронний навч. посіб. Вінниця: ВНТУ, 2015, 127 с.
А. І. Зубик, ГІС в урбаністиці та просторовому плануванні, навчальнометод. посіб. для аудиторної та самостійної роботи студентів з курсу «Використання ГІС в урбаністиці та просторовому плануванні». Львів, 2021, 580 с.
В. Б. Мокін, І. В. Варчук, і Є. М. Крижановський, Інформаційна технологія аналізу та оптимізації топологічної спостережуваності багатозв’язних геоінформаційних систем: моногр. Вінниця: ВНТУ, 2019, с.
Qi Chen, et. al, “TEMPORARY REMOVAL: Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 147, pp. 42-55, 2019. ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2018.11.011 .
Q. Li, et al., “Instance Segmentation of Buildings Using Keypoints,” IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 1452-1455, https://doi.org/10.1109/IGARSS39084.2020.9324457 .
M. Guo, H. Liu, Y. Xu, and Y. Huang, Building Extraction Based on U-Net with an Attention Block and Multiple Losses. Remote sensing (Basel, Switzerland), no. 12(9), pp.1400, 2020. https://doi.org/10.3390/rs12091400 .
Sariturk, Batuhan, et al. “Feature Extraction from Satellite Images Using Segnet and Fully Convolutional Networks (FCN),” International Journal of Engineering and Geosciences, vol. 5, no. 3, Oct. 2020.
Gaston Lenczner, Adrien Chan-Hon-Tong, Bertrand Le Saux, Nicola Luminari, and Guy Le Besnerais, “DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, no. 15, pp. 3376-3389, 2022, ff10.1109/jstars.2022.3166551f, https://ieeexplore.ieee.org/document/9324457.
Agustsson, E., et. al, “Interactive full image segmentation by considering all regions jointly,” in CVPR. pp. 11622-11631. IEEE, 2019.
Ю. О. Карпінський, А. А. Лященко, і Н. Ю. Лазоренко-Гевель., Основи ГІС. Стандартизація географічної інформації, навч. посіб. Київ, Україна: КНУБА, 2021, 152 с.
В. Б. Мокін, і О. В. Коменчук, «Сучасні інформаційні технології для розпізнавання дахів будівель на аерофотозйомці,» у Матеріали LI Науково-технічної конференції факультету інтелектуальних інформаційних технологій та автоматизації Вінницького національного технічного університету, Вінниця, 31 травня – 1 червня 2022 р. [Електронний ресурс]. Режим доступу: https://conferences.vntu.edu.ua/index.php/all-fksa/all-fksa-2022/paper/view/16150/13565 .
М. В. Дратований, і В. Б. Мокін, «Інтелектуальний метод з підкріпленням синтезу оптимального конвеєру операцій попереднього оброблення даних у задачах машинного навчання,» Наукові праці ВНТУ, вип. 4, Груд. 2022. [Електронний ресурс]. Режим доступу: https://praci.vntu.edu.ua/index.php/praci/article/view/670/631 .
“AIRS (Aerial Imagery for Roof Segmentation),” Kaggle, [Electronic resource]. Available: https://www.kaggle.com/datasets/atilol/aerialimageryforroofsegmentation/data .
“Sydney Roofers, Roofing Company Sydney,” Rooflines, [Electronic resource]. Available: https://www.rooflines.com.au/blog/6-most-common-australian-roof-types .
Nexe-ua, «Про монтаж черепиці,» [Electronic resource]. Available: https://nexe-ua.com/ua/pro-montazh-cherepici/ .
GitHub, “DE⫶TR: End-to-End Object Detection with Transformers,” [Electronic resource]. Available: https://github.com/facebookresearch/detr .
Hans Thisanke, Chamli Deshan, Kavindu Chamith, Sachith Seneviratne, Rajith Vidanaarachchi, and Damayanthi Herath, Semantic Segmentation using Vision Transformers: A survey. 2023, https://doi.org/10.48550/arXiv.2305.03273 .
“Semantic Segmentation on ADE20K,” Paperswithcode, [Online]. Available: https://paperswithcode.com/sota/semantic-segmentation-on-ade20k .
“Object Detection on COCO test-dev,” Paperswithcode, [Electronic resource]. Available: https://paperswithcode.com/sota/object-detection-on-coco .
“Segment Anything,” GitHub, [Online]. Available: https://github.com/facebookresearch/segment-anything .
Downloads
-
PDF (Українська)
Downloads: 63
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
