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Automated Processing of Remote Sensing Imagery Using Deep Semantic Segmentation: A Building Footprint Extraction Case

Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia
ISPRS Int. J. Geo-Inf. 2020, 9(8), 486; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080486
Received: 17 July 2020 / Revised: 4 August 2020 / Accepted: 10 August 2020 / Published: 11 August 2020
The proliferation of high-resolution remote sensing sensors and platforms imposes the need for effective analyses and automated processing of high volumes of aerial imagery. The recent advance of artificial intelligence (AI) in the form of deep learning (DL) and convolutional neural networks (CNN) showed remarkable results in several image-related tasks, and naturally, gain the focus of the remote sensing community. In this paper, we focus on specifying the processing pipeline that relies on existing state-of-the-art DL segmentation models to automate building footprint extraction. The proposed pipeline is organized in three stages: image preparation, model implementation and training, and predictions fusion. For the first and third stages, we introduced several techniques that leverage remote sensing imagery specifics, while for the selection of the segmentation model, we relied on empirical examination. In the paper, we presented and discussed several experiments that we conducted on Inria Aerial Image Labeling Dataset. Our findings confirmed that automatic processing of remote sensing imagery using DL semantic segmentation is both possible and can provide applicable results. The proposed pipeline can be potentially transferred to any other remote sensing imagery segmentation task if the corresponding dataset is available. View Full-Text
Keywords: remote sensing imagery; deep learning; semantic segmentation; building extraction; convolutional neural networks remote sensing imagery; deep learning; semantic segmentation; building extraction; convolutional neural networks
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MDPI and ACS Style

Milosavljević, A. Automated Processing of Remote Sensing Imagery Using Deep Semantic Segmentation: A Building Footprint Extraction Case. ISPRS Int. J. Geo-Inf. 2020, 9, 486. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080486

AMA Style

Milosavljević A. Automated Processing of Remote Sensing Imagery Using Deep Semantic Segmentation: A Building Footprint Extraction Case. ISPRS International Journal of Geo-Information. 2020; 9(8):486. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080486

Chicago/Turabian Style

Milosavljević, Aleksandar. 2020. "Automated Processing of Remote Sensing Imagery Using Deep Semantic Segmentation: A Building Footprint Extraction Case" ISPRS International Journal of Geo-Information 9, no. 8: 486. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080486

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