Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile

Abstract

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatio-temporal variability of elements at risk, detailed information is costly both in terms of time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very high resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed, however, the design of information extraction procedures with a high level of automatisation remains challenging. In this paper, we uniquely combine remote sensing data and Volunteered Geographic Information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in

Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations

“…However, these data sources reflect the increasingly volumes of data that are routinely in open and free access to high-quality national mapping agency data, providing highly consistent ancillary data resources to support dasymetric interpolation (Langford, 2013). User-contributed information and VGI provide alternative sources of ancillary data for dasymetric interpolation, as used in a number of studies (Bakillah et al, 2014;Kunze & Hecht, 2015;Geiß et al, 2017). Although there are potential quality issues, these provide valuable data sources that can complement official and commercial data (Goodchild, 2007;Bakillah et al, 2014).…”

Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

“…However, it is also possible to generate entirely new remote sensing data products using VGI. For example, collecting high quality and up-to-date authoritative training data for supervised remote sensing image classification can be expensive, time consuming, and difficult [114], so recently, some studies [114][115][116][117][118][119][120][121] have explored the feasibility of using VGI as an alternative source of training data. While the majority of the previous studies deployed the OpenStreetMap (https://www.openstreetmap.org) (OSM) crowdsourced dataset [114][115][116] as an alternative nonauthoritative source for teaching the different supervised image classification algorithms to produce land use/land cover (LULC) maps, a few studies explored other fully or partially crowdsourced data sources such as the Global Biodiversity Information Facility (https://www.gbif.org) (GBIF) dataset (e.g., see [119]) or Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) dataset (e.g., see [120]) for these means.…”

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

“…A deterministic framework to create land use and land cover maps from crowdsourced maps such as OSM data was proposed in [10]. Machine learning tools (a random forest variant) also allow coupling remote sensing and volunteered geographic information (VGI) to predict natural hazard exposure [11] and local climate zones [8], while active deep learning helps finding unlabeled objects in OSM [5]. However, to the best of our knowledge, no VGI has been used as an input (opposite of a target) in deep neural networks yet.…”