Fine-Scale Urban Informal Settlements Mapping by Fusing Remote Sensing Images and Building Data via a Transformer-Based Multimodal Fusion Network DOI
Runyu Fan, Fengpeng Li, Wei Han

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2022, Volume and Issue: 60, P. 1 - 16

Published: Jan. 1, 2022

Urban informal settlements (UIS) are high-density population with low standards of living and supply. UIS semantic segmentation, which identifies pixels corresponding to in remote sensing images, is crucial the estimation poor communities, urban management, resource allocation, future planning, particularly megacities. However, most studies on settlement mapping either based parcels (image classification) or (semantic segmentation). Few utilize object information improve mapping. Since formed by buildings (objects), utilizing can segmentation. Furthermore, current mainly focus using single-modality there a lack related research multimodal data. Due spatial heterogeneity settlements, only single modality image features limits effectiveness accuracy Aiming at achieving fine-scale results, this paper proposes segmentation method, namely UisNet, that utilizes transformer-based block receive data, including high-spatial-resolution images (parcel- pixel-level) building polygon data (object-level) identify UIS. The experiments were conducted Shenzhen City, they confirmed superior performance achieved an overall (OA) 94.80% mean intersection over union (mIoU) 85.51% testing set manually labeled dataset (UIS-Shenzhen dataset) outperformed best models tasks. Besides, we add public (GID compare our method state-of-the-art methods. Experiments show proposed UisNet improves mIoU 1.64% 7.58% compared other This work will be available https://github.com/RunyuFan/.

Language: Английский

Deep learning in multimodal remote sensing data fusion: A comprehensive review DOI Creative Commons
Jiaxin Li, Danfeng Hong, Lianru Gao

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 112, P. 102926 - 102926

Published: July 26, 2022

With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity are readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications fresh way. joint utilization EO data, much research on multimodal RS fusion has made tremendous progress recent years, yet these developed traditional algorithms inevitably meet performance bottleneck due lack ability comprehensively analyze interpret strongly heterogeneous data. Hence, this non-negligible limitation further arouses intense demand for alternative tool with powerful processing competence. Deep learning (DL), as cutting-edge witnessed remarkable breakthroughs numerous computer vision tasks owing its impressive representation reconstruction. Naturally, it been successfully applied field fusion, yielding improvement compared methods. This survey aims present systematic overview DL-based fusion. More specifically, some essential knowledge about topic is first given. Subsequently, literature conducted trends field. Some prevalent sub-fields then reviewed terms to-be-fused modalities, i.e., spatiospectral, spatiotemporal, light detection ranging-optical, synthetic aperture radar-optical, RS-Geospatial Big Data Furthermore, We collect summarize valuable resources sake development Finally, remaining challenges potential future directions highlighted.

Language: Английский

Citations

309
Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change DOI
P. S. Roy, Reshma M. Ramachandran,

Oscar Paúl

et al.

Journal of the Indian Society of Remote Sensing, Journal Year: 2022, Volume and Issue: 50(8), P. 1615 - 1640

Published: June 7, 2022

Language: Английский

Citations

145
A review of spatially-explicit GeoAI applications in Urban Geography DOI Creative Commons
Pengyuan Liu, Filip Biljecki

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 112, P. 102936 - 102936

Published: Aug. 1, 2022

Urban Geography studies forms, social fabrics, and economic structures of cities from a geographic perspective. Catalysed by the increasingly abundant spatial big data, seeks new models research paradigms to explain urban phenomena address issues. Recent years have witnessed significant advances in spatially-explicit geospatial artificial intelligence (GeoAI), which integrates AI, primarily focusing on incorporating thinking concept into deep learning for studies. This paper provides an overview techniques applications GeoAI based 581 papers identified using systematic review approach. We examined screened three scopes (Urban Dynamics, Social Differentiation Areas, Sensing) found that although is trending topic geography neural network-based methods are proliferating, development still at their early phase. challenges existing advised future direction towards developing multi-scale explainable GeoAI. acquaints beginners with basics state-of-the-art serve as inspiration attract more exploring potential studying socio-economic dimension city life.

Language: Английский

Citations

103
Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades DOI Creative Commons
Danlin Yu, Chuanglin Fang

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(5), P. 1307 - 1307

Published: Feb. 26, 2023

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite unmanned drone and hyperspectral among many others, have provided fresh opportunities to examine dynamics of urban landscapes. In meantime, rapid development telecommunications mobile technology, alongside emergence online search engines social media platforms with geotagging has fundamentally changed how human activities landscape are recorded depicted. The combination these two types sources results in explosive mind-blowing discoveries contemporary studies, especially for purposes sustainable planning development. Urban scholars now equipped abundant theoretical arguments that often result from limited indirect observations less-than-ideal controlled experiments. For first time, can model, simulate, predict changes using real-time produce most realistic results, providing invaluable information planners governments aim a healthy future. This current study reviews development, status, future trajectory studies facilitated by advancement big analytical technologies. review attempts serve as bridge between growing “big data” modern communities.

Language: Английский

Citations

64
A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing DOI
Yanzhao Wang, Yonghua Sun, X. L. Cao

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 206, P. 311 - 334

Published: Nov. 28, 2023

Language: Английский

Citations

64
Multiscale Location Attention Network for Building and Water Segmentation of Remote Sensing Image DOI
Xin Dai, Min Xia, Liguo Weng

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2023, Volume and Issue: 61, P. 1 - 19

Published: Jan. 1, 2023

Traditional building and water segmentation methods are vulnerable to noise interference, hence they could not avoid missed false detections in the detection process. Excessive deep learning downsampling would lead significant loss of feature map information, image location information offset, overall effect falling apart. To address these issues, a Multi-Scale Location Attention Network (MSLA) is proposed. Location-spatial channel particularly important for edge detail cover. The network includes Channel Unit (LCA) focus on tributary details rivers eaves. Moreover, this paper builds Dual-Branch Aggregation (DBMSA) obtain deeper multi-scale semantic information. Finally, Fusion (MSF) used guide merging multiple stages, boundary improved by splicing acquired with relevant extraction layer downsampling. experimental results several datasets show that proposed approach outperforms other methodologies accuracy.

Language: Английский

Citations

50
Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image DOI
Yan Zhang, Pengyuan Liu, Filip Biljecki

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 198, P. 153 - 168

Published: March 16, 2023

Language: Английский

Citations

45
Remote Sensing Change Detection With Transformers Trained From Scratch DOI

Mubashir Noman,

Mustansar Fiaz, Hisham Cholakkal

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 14

Published: Jan. 1, 2024

Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely first pre-training another CD and then fine-tuning the target benchmark. This current strategy is driven by fact that transformers typically require large amount of training data to learn inductive biases, which insufficient in standard datasets due their small size. We develop an end-to-end approach with from scratch yet achieves state-of-the-art performance five benchmarks. Instead using conventional self-attention struggles capture biases when scratch, our architecture utilizes shuffled sparse-attention operation focuses selected sparse informative regions inherent characteristics data. Moreover, we introduce change-enhanced feature fusion (CEFF) module fuse features input pairs performing per-channel re-weighting. Our CEFF aids enhancing relevant semantic changes while suppressing noisy ones. Extensive experiments reveal merits proposed contributions, achieving gains as high 1.35% intersection over union (IoU) score, compared best-published results literature. Code available at https://github.com/mustansarfiaz/ScratchFormer.

Language: Английский

Citations

31
Trustworthy remote sensing interpretation: Concepts, technologies, and applications DOI
Sheng Wang, Wei Han, Xiaohui Huang

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 209, P. 150 - 172

Published: Feb. 8, 2024

Language: Английский

Citations

26
Coupling coordination analysis of urbanization and ecological environment in Chengdu-Chongqing urban agglomeration DOI Creative Commons
Xiangqi Lei, Hanhu Liu,

Shaoda Li

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 161, P. 111969 - 111969

Published: April 1, 2024

As the fourth pole of China's economic growth, Chengdu-Chongqing urban agglomeration plays a significant role in reinforcing ecological barrier upper reaches Yangtze River, and is crucial for environmental protection strategies. In this paper, Google Earth Engine (GEE) MODIS images from 2000, 2005, 2010, 2015, 2022 were utilized to construct Improved Remote Sensing Ecological Index (IRSEI) characterize quality more accurately than RSEI. Additionally, combined with nighttime light remote sensing data, land use data socio-economic GDP sub-industry spatialization model was analyze urbanization process depth. To dynamically monitor evaluate interaction between environment quality, coupling coordination incorporating above methods developed. The results show that (1) effective information IRSEI analysis increased by 3.26% compared RSEI, correlation each index higher; (2) peaked 2005 has been declining since then, rate decline gradually slowing down 2022; (3) suitable characterizing scattered villages, can effectively exhibit process. From 2000 2022, rapidly developed, level core cities such as Chengdu Chongqing far exceeded those neighboring cities; (4) generally increased, indicating ongoing improvements synergy agglomeration. This study developed method quickly monitoring assessing relationship using model, provides scientific analytical support governing emerging agglomerations.

Language: Английский

Citations

18