Abstract
The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.
Highlights
It is natural to ask how the growing amount of freely available spatio-temporal information, such as aerial imagery from the National Agriculture Imagery Program (NAIP), can be leveraged to support and guide OpenStreetMap (OSM) mappers in their work
The contribution of this study is summarized as follows: 1. We demonstrate the application of a modified CycleGAN with an attention mechanism trained on NAIP vertical aerial imagery and OSM raster tiles
To quantify the model’s accuracy, we focused on the ratio R of false negatives (FN) versus true positives (TP) with respect to building detection
Summary
It is natural to ask how the growing amount of freely available spatio-temporal information, such as aerial imagery from the National Agriculture Imagery Program (NAIP), can be leveraged to support and guide OpenStreetMap (OSM) mappers in their work. This paper aims at generating visual guidance for OSM [1] mappers to support their labeling efforts by programmatically identifying regions of interest where OSM is likely to require updating. The outlined approach is based on translating remote sensing data, in particular vertical aerial images, into estimated OSMs for the regions contained in the image tiles. This first step exploits an image-to-image translation technique well studied in the deep learning domain. The estimated OSM is compared to the current map to produce a “heat map”, highlighting artifacts and structures that alert mappers to locations that require updates in the current OSM. The output provides computer-accelerated assistance for the labeling work of OSM volunteers
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