The World Bank is interested in conducting least-cost electrification studies in developing countries with a view toward universal electricity access. Accurate and up-to-date knowledge of existing electrical transmission grid infrastructure is required for this purpose. To improve the quality of this data NEO has developed a novel smart-tracing algorithm to detect and trace electrical towers in Very High Resolution (VHR) satellite imagery. This smart-tracing approach uses existing open datasets alongside a deep learning model for object detection. The method is scalable and adaptable to arbitrary regions with satellite image coverage.
This paper presents a system for linking Earth Observation with open Web data, into a Linked Open Data architecture. The architecture has two components, one for extracting signals from Earth Observation data, and another for harvesting web sources. Both are linked with spatial objects. Web scraped data, either from APIs or crowd-sourced websites are geo-referenced and thematically annotated with standard vocabularies. The architecture has been demonstrated in two case studies, one for building permits and another for crowd-sourced observations of invasive aquatic plants.
Agricultural fields are monitored for the purpose of EU subsidy eligibility checks. A precondition to make automatic monitoring of fields for this purpose possible is that the object geometric boundary is correct. This precondition can be addressed to some extent by performing image time series analysis to identify changes. Accurate object change detection in agricultural fields on satellite images requires separating object class changes such as new ditches, buildings, or roads from other changes, such as crop development, crop management practices, seasonal variation, or shadows from adjacent objects. In this paper we present an approach to identify unchanged agricultural fields using Deep Neural Networks. We propose a combination of CNNs for semantic segmentation and ConvLSTMs for change detection, applied to multitemporal satellite image time-series of arbitrary length. The neural networks were trained on images acquired over the Netherlands in 2017 by the TripleSat and PlanetScope constellations (0.8 and 3.5m resolution respectively) with RGB and NIR bands. We introduce techniques to create artificial change training data, reducing the need for real training data. The results demonstrate that (1) a neural network is not required to be deep to achieve usable semantic segmentation performance for satellite images for this application, that (2) ConvLSTMs can to some extent compensate imperfect image alignment and pixel misclassification, that (3) longer time series significantly increase the performance of the change detection and that (4) expanding and densifying the time series with lower resolution imagery does not improve accuracy in this particular configuration.
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