2,855 research outputs found
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
Deep learning methods applied to digital elevation models: state of the art
Deep Learning (DL) has a wide variety of applications in various
thematic domains, including spatial information. Although with
limitations, it is also starting to be considered in operations
related to Digital Elevation Models (DEMs). This study aims to
review the methods of DL applied in the field of altimetric spatial
information in general, and DEMs in particular. Void Filling (VF),
Super-Resolution (SR), landform classification and hydrography
extraction are just some of the operations where traditional methods
are being replaced by DL methods. Our review concludes
that although these methods have great potential, there are
aspects that need to be improved. More appropriate terrain information
or algorithm parameterisation are some of the challenges
that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103
Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge
In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge
The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries
Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups
Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics
Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach
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