Language: | Slovenian |
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Year of publishing: | 2020 |
Typology: | 2.09 - Master's Thesis |
Organization: | UL FGG - Faculty of Civil and Geodetic Engineering |
Publisher: | [S. Šanca] |
UDC: | 004.032.26:528.8(497.4)(043.3) |
COBISS: | 37248771 |
Views: | 570 |
Downloads: | 210 |
Average score: | 0 (0 votes) |
Metadata: |
Secondary language: | English |
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Secondary title: | Automatic classification of buildings with deep learning |
Secondary abstract: | Multispectral satellite or aerial images provide detailed information about the Earth%s surface. Multispectal image based classification is one of the fundamental tasks in the field of remote sensing. Building data is organized in the buidling cadastre, which needs to be regularly updated. As alternative methods for building cadastre maintenance computer vision and machine learning can be used. In recent years deep learning with the emphasis on convolutional neural networks are in the forefront for automatic classification of buildings. We applied the region based convolutional framework called Mask Region Based Convolutional Neural Network (Mask R-CNN) for automatic building classification and developed a dataset in the Microsoft Common Objects in Context (MS COCO) format. The building dataset was used for the training of the models on near infrared aerial images from the last aerial imaging of Slovenia in year 2019. The proposed method was tested and evaluated on selected areas in Slovenia. The results show that automatic classification of buildings with deep learning is suitable for building detection and can be used either as a replacement of current techniques or to aid the existing ones. |
Secondary keywords: | geodesy;master thesis;deep learning;convolutional neural networks;classification of buildings;CAS;Mask R-CNN;object detection;object segmentation;automatic classification; |
Type (COBISS): | Master's thesis/paper |
Thesis comment: | Univ. v Ljubljani, Fak. za gradbeništvo in geodezijo |
Pages: | XVIII, 45 str. |
ID: | 12379716 |