2,793 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
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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
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