56,740 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Satellite Imagery Multiscale Rapid Detection with Windowed Networks
Detecting small objects over large areas remains a significant challenge in
satellite imagery analytics. Among the challenges is the sheer number of pixels
and geographical extent per image: a single DigitalGlobe satellite image
encompasses over 64 km2 and over 250 million pixels. Another challenge is that
objects of interest are often minuscule (~pixels in extent even for the highest
resolution imagery), which complicates traditional computer vision techniques.
To address these issues, we propose a pipeline (SIMRDWN) that evaluates
satellite images of arbitrarily large size at native resolution at a rate of >
0.2 km2/s. Building upon the tensorflow object detection API paper, this
pipeline offers a unified approach to multiple object detection frameworks that
can run inference on images of arbitrary size. The SIMRDWN pipeline includes a
modified version of YOLO (known as YOLT), along with the models of the
tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed
approach allows comparison of the performance of these four frameworks, and can
rapidly detect objects of vastly different scales with relatively little
training data over multiple sensors. For objects of very different scales (e.g.
airplanes versus airports) we find that using two different detectors at
different scales is very effective with negligible runtime cost.We evaluate
large test images at native resolution and find mAP scores of 0.2 to 0.8 for
vehicle localization, with the YOLT architecture achieving both the highest mAP
and fastest inference speed.Comment: 8 pages, 7 figures, 2 tables, 1 appendix. arXiv admin note:
substantial text overlap with arXiv:1805.0951
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
GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images
Automatic extraction of buildings in remote sensing images is an important
but challenging task and finds many applications in different fields such as
urban planning, navigation and so on. This paper addresses the problem of
buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS)
images, whose spatial resolution is often up to half meters and provides rich
information about buildings. Based on the observation that buildings in VHSR-RS
images are always more distinguishable in geometry than in texture or spectral
domain, this paper proposes a geometric building index (GBI) for accurate
building extraction, by computing the geometric saliency from VHSR-RS images.
More precisely, given an image, the geometric saliency is derived from a
mid-level geometric representations based on meaningful junctions that can
locally describe geometrical structures of images. The resulting GBI is finally
measured by integrating the derived geometric saliency of buildings.
Experiments on three public and commonly used datasets demonstrate that the
proposed GBI achieves the state-of-the-art performance and shows impressive
generalization capability. Additionally, GBI preserves both the exact position
and accurate shape of single buildings compared to existing methods
- …