Integrating machine learning techniques and high-resolution imagery to generate GIS-ready information for urban water consumption studies

Nils Wolf; Angela Hof

doi:10.1117/12.977789

25 October 2012 Integrating machine learning techniques and high-resolution imagery to generate GIS-ready information for urban water consumption studies

Nils Wolf, Angela Hof

Author Affiliations +

Proceedings Volume 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III; 85380H (2012) https://doi.org/10.1117/12.977789
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

Urban sprawl driven by shifts in tourism development produces new suburban landscapes of water consumption on Mediterranean coasts. Golf courses, ornamental, 'Atlantic' gardens and swimming pools are the most striking artefacts of this transformation, threatening the local water supply systems and exacerbating water scarcity. In the face of climate change, urban landscape irrigation is becoming increasingly important from a resource management point of view. This paper adopts urban remote sensing towards a targeted mapping approach using machine learning techniques and highresolution satellite imagery (WorldView-2) to generate GIS-ready information for urban water consumption studies. Swimming pools, vegetation and – as a subgroup of vegetation – turf grass are extracted as important determinants of water consumption. For image analysis, the complex nature of urban environments suggests spatial-spectral classification, i.e. the complementary use of the spectral signature and spatial descriptors. Multiscale image segmentation provides means to extract the spatial descriptors – namely object feature layers – which can be concatenated at pixel level to the spectral signature. This study assesses the value of object features using different machine learning techniques and amounts of labeled information for learning. The results indicate the benefit of the spatial-spectral approach if combined with appropriate classifiers like tree-based ensembles or support vector machines, which can handle high dimensionality. Finally, a Random Forest classifier was chosen to deliver the classified input data for the estimation of evaporative water loss and net landscape irrigation requirements.

Citation Download Citation

Nils Wolf and Angela Hof "Integrating machine learning techniques and high-resolution imagery to generate GIS-ready information for urban water consumption studies", Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380H (25 October 2012); https://doi.org/10.1117/12.977789

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