ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 267–274, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-267-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 267–274, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-267-2022
 
18 May 2022
18 May 2022

AN INFORMAL ROAD DETECTION NEURAL NETWORK FOR SOCIETAL IMPACT IN DEVELOPING COUNTRIES

I. Fabris-Rotelli1, A. Wannenburg1, G. Maribe1, R. Thiede1, M. Vogel1, M. Coetzee1, K. Sethaelo1, E. Selahle1, P. Debba3,4, and V. Rautenbach2 I. Fabris-Rotelli et al.
  • 1Department of Statistics, University of Pretoria, Pretoria, South Africa
  • 2Department of Geography, Geoinformatics and Meteorology, Univeristy of Pretoria, South Africa
  • 3Council for Scientific and Industrial Research, Pretoria, South Africa
  • 4Department of Statistics and Actuarial Science, University of Witwatersrand Johannesburg, South Africa

Keywords: deep learning, informal roads, road extraction, neural networks, South Africa

Abstract. Roads found in informal settlements arise out of convenience, and are often not recorded or maintained by authorities. This complicates service delivery, sustainable development and crisis mitigation, including management and tracking of COVID-19. We, therefore, aim to extract informal roads in remote sensing images. Existing techniques aiming at the extraction of formal roads are not suitable for the problem due to the complex physical and spectral properties of informal roads. The only existing approaches for informal roads, namely (Nobrega et al., 2006, Thiede et al., 2020), do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities. However, they require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data. This paper implements a neural network to extract informal roads from a data set digitised by this research group. Data quality is assessed by calculating validity completeness, homogeneity and the V-measure, a measure of consistency, in order to evaluate the overall usability of the dataset for neural network informal road detection. We implement the GANs-UNet model that obtained the highest F1-score in a 2020 review paper (Abdollahi et al., 2020) on the state-of-the-art deep learning models used to extract formal roads. The results indicate that the model is able to extract informal roads successfully in the presence of appropriate training data.