8,025 research outputs found
Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators
Solar panels are installed by a large and growing number of households due to
the convenience of having cheap and renewable energy to power house appliances.
In contrast to other energy sources solar installations are distributed very
decentralized and spread over hundred-thousands of locations. On a global level
more than 25% of solar photovoltaic (PV) installations were decentralized. The
effect of the quick energy transition from a carbon based economy to a green
economy is though still very difficult to quantify. As a matter of fact the
quick adoption of solar panels by households is difficult to track, with local
registries that miss a large number of the newly built solar panels. This makes
the task of assessing the impact of renewable energies an impossible task.
Although models of the output of a region exist, they are often black box
estimations. This project's aim is twofold: First automate the process to
extract the location of solar panels from aerial or satellite images and
second, produce a map of solar panels along with statistics on the number of
solar panels. Further, this project takes place in a wider framework which
investigates how official statistics can benefit from new digital data sources.
At project completion, a method for detecting solar panels from aerial images
via machine learning will be developed and the methodology initially developed
for BE, DE and NL will be standardized for application to other EU countries.
In practice, machine learning techniques are used to identify solar panels in
satellite and aerial images for the province of Limburg (NL), Flanders (BE) and
North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various
datasets which will be used throughout the project. The collection of
satellite and aerial images as well as auxiliary information such as the
location of buildings and roofs which is required to train, test and validate
the machine learning algorithm that is being develope
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
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