Related papers: A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks

A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks

URL: http://arxiv.org/abs/2001.11935v1
Date: Fri, 31 Jan 2020 16:23:34 GMT
Title: A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks
Authors: C. Qiu and M. Schmitt and C. Geiss and T. K. Chen and X. X. Zhu
Abstract summary: Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization. This paper presents a deep-learning-based framework to automatically map HSE from multi-spectral Sentinel-2 data. The framework is validated against both manually labelled checking points distributed evenly over the test areas, and the OpenStreetMap building layer.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping HSE is critical for various environmental issues at local, regional, and even global scales. This paper presents a deep-learning-based framework to automatically map HSE from multi-spectral Sentinel-2 data using regionally available geo-products as training labels. A straightforward, simple, yet effective fully convolutional network-based architecture, Sen2HSE, is implemented as an example for semantic segmentation within the framework. The framework is validated against both manually labelled checking points distributed evenly over the test areas, and the OpenStreetMap building layer. The HSE mapping results were extensively compared to several baseline products in order to thoroughly evaluate the effectiveness of the proposed HSE mapping framework. The HSE mapping power is consistently demonstrated over 10 representative areas across the world. We also present one regional-scale and one country-wide HSE mapping example from our framework to show the potential for upscaling. The results of this study contribute to the generalization of the applicability of CNN-based approaches for large-scale urban mapping to cases where no up-to-date and accurate ground truth is available, as well as the subsequent monitor of global urbanization.

Related papers

Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
arXiv Detail & Related papers (2023-09-26T23:55:39Z)
GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
arXiv Detail & Related papers (2023-03-27T17:59:34Z)
A Coarse-to-Fine Approach for Urban Land Use Mapping Based on Multisource Geospatial Data [4.2968261363970095]
We propose a machine learning-based approach for parcel-level urban land use mapping. We first divide the city into built-up and non-built-up regions based on parcels generated from road networks. We then adopt different classification strategies for parcels in different regions, and finally combine the classified results into an integrated land use map.
arXiv Detail & Related papers (2022-08-18T13:30:56Z)
A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation [52.911307452212256]
We develop a unified architecture of semantic segmentation and hierarchical GANs. A unique advantage of our framework is that on forward pass the semantic segmentation network conditions the generative model. We evaluate our method on two challenging facial expression translation benchmarks, AffectNet and RaFD, and a semantic segmentation benchmark, CelebAMask-HQ.
arXiv Detail & Related papers (2021-12-08T22:06:31Z)
Map completion from partial observation using the global structure of multiple environmental maps [4.627706451989238]
This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation.
arXiv Detail & Related papers (2021-03-16T13:48:37Z)
Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity [0.0]
We present a new semantic segmentation model for historical city maps based on convolutional neural networks. We show that these networks are able to semantically segment map data of a very large figurative diversity with efficiency.
arXiv Detail & Related papers (2021-01-29T09:08:12Z)
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation [87.50205728818601]
We propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information.
arXiv Detail & Related papers (2020-11-25T11:03:11Z)
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
arXiv Detail & Related papers (2020-09-19T09:26:20Z)
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset [14.588626423336024]
This study is based on the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. In addition, we proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this base network, we propose fusing multi-level features using the extended Sen2LCZ-Net-MF. Our work will provide important baselines for future CNN-based algorithm developments for both LCZ classification and other urban land cover land use classification.
arXiv Detail & Related papers (2020-05-16T13:16:47Z)
Phase Consistent Ecological Domain Adaptation [76.75730500201536]
We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
arXiv Detail & Related papers (2020-04-10T06:58:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.