3,089 research outputs found
GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data
abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201
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
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
The past years have witnessed great progress on remote sensing (RS) image
interpretation and its wide applications. With RS images becoming more
accessible than ever before, there is an increasing demand for the automatic
interpretation of these images. In this context, the benchmark datasets serve
as essential prerequisites for developing and testing intelligent
interpretation algorithms. After reviewing existing benchmark datasets in the
research community of RS image interpretation, this article discusses the
problem of how to efficiently prepare a suitable benchmark dataset for RS image
interpretation. Specifically, we first analyze the current challenges of
developing intelligent algorithms for RS image interpretation with bibliometric
investigations. We then present the general guidances on creating benchmark
datasets in efficient manners. Following the presented guidances, we also
provide an example on building RS image dataset, i.e., Million-AID, a new
large-scale benchmark dataset containing a million instances for RS image scene
classification. Several challenges and perspectives in RS image annotation are
finally discussed to facilitate the research in benchmark dataset construction.
We do hope this paper will provide the RS community an overall perspective on
constructing large-scale and practical image datasets for further research,
especially data-driven ones
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Deep learning has achieved great success in learning features from massive
remote sensing images (RSIs). To better understand the connection between
feature learning paradigms (e.g., unsupervised feature learning (USFL),
supervised feature learning (SFL), and self-supervised feature learning
(SSFL)), this paper analyzes and compares them from the perspective of feature
learning signals, and gives a unified feature learning framework. Under this
unified framework, we analyze the advantages of SSFL over the other two
learning paradigms in RSIs understanding tasks and give a comprehensive review
of the existing SSFL work in RS, including the pre-training dataset,
self-supervised feature learning signals, and the evaluation methods. We
further analyze the effect of SSFL signals and pre-training data on the learned
features to provide insights for improving the RSI feature learning. Finally,
we briefly discuss some open problems and possible research directions.Comment: 24 pages, 11 figures, 3 table
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