The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track DSE: Detection of Settlements Without Electricity

Ma, Yanbiao; Li, Yuxin; Feng, Kexin; Xia, Yu; Huang, Qi; Zhang, Hongyan; Prieur, Colin; Licciardi, Giorgio; Malha, Hana; Chanussot, Jocelyn; Ghamisi, Pedram; Hänsch, Ronny; Yokoya, Naoto

doi:10.1109/jstars.2021.3130446

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…There are seven commonly used DL strategies for existing STF methods: residual learning, attention mechanism, super-resolution, multi-stream, composite loss function, multi-scale mechanism, and migration learning. The main applications of DL techniques in STF are land cover classification [33,[50][51][52][53][54][55][56][57][58][59], change detection [60][61][62][63][64][65][66][67][68][69], and multi-sensor data fusion [70][71][72][73][74]. "Spatial analysis" and "GIS" had a total of 443 publications in 2017-2023, and the synergy between STF, spatial analysis, and the Geographic Information System (GIS) provides more integrated and fine-grained tools and methods.…”

Section: Modelsmentioning

confidence: 99%

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

Cui,

Li,

et al. 2024

Atmosphere

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Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords “Sentinel”, “deep learning” (DL), and “LSTM” (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images’ accuracy.

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Section: Modelsmentioning

confidence: 99%

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

Cui,

Li,

et al. 2024

Atmosphere

View full text Add to dashboard Cite

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“…A The contest is designed as a benchmark competi tion following the previous editions [1], [2], [3], [4], [5], [6], [7] and will consist of the following two paral lel tracks: 1) " The participants are required to reconstruct building heights and extract building footprints. Figure 3 shows an example.…”

Section: ◗ Globally Distributed and Large Scalementioning

confidence: 99%

2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees]

Persello¹,

Hänsch²,

Vivone³

et al. 2023

IEEE Geosci. Remote Sens. Mag.

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“…The ultimate goal of the competition is to develop accurate building extraction and classification models using multimodal data, including optical and synthetic aperture radar (SAR) images. This contest was designed as a benchmarking competition following previous editions [12], [13], [14], [15], [16], [17], [18]. DFC23 featured the following two tracks running in parallel: 1) " sequence of points that delineates the building's contours) or run-length encoding, one fine-grained category with confidence for each instance, and one optional bounding box.…”

Section: The 2023 Data Fusion Contestmentioning

confidence: 99%

Report on the 2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees]

Hänsch,

Persello,

Vivone

et al. 2023

IEEE Geosci. Remote Sens. Mag.

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T he Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aims to propose new problem settings that are challenging to address with existing techniques, and to establish new benchmarks for scientific challenges in remote sensing image analysis [1

show abstract

The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track DSE: Detection of Settlements Without Electricity

Cited by 13 publications

References 32 publications

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools

2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees]

Report on the 2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees]

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