Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part B

Lian, Yanchao; Feng, Tuo; Zhou, Jinliu; Jia, Meixia; Li, Aijin; Wu, Zhaoyang; Jiao, Licheng; Brown, Myron; Hager, Gregory D.; Yokoya, Naoto; Hänsch, Ronny; Saux, Bertrand Le

doi:10.1109/jstars.2020.3035274

Cited by 24 publications

(14 citation statements)

References 30 publications

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“…With F1 scores over 97%, we reached the limits of ground truth quality [86]. Similar results can be expected from other point cloud classification networks [84,86,119].…”

Section: Stem Precision and Recallsupporting

confidence: 87%

Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks

2022

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Since trees are a vital part of urban green infrastructure, automatic mapping of individual urban trees is becoming increasingly important for city management and planning. Although deep-learning-based object detection networks are the state-of-the-art in computer vision, their adaptation to individual tree detection in urban areas has scarcely been studied. Some existing works have employed 2D object detection networks for this purpose. However, these have used three-dimensional information only in the form of projected feature maps. In contrast, we exploited the full 3D potential of airborne laser scanning (ALS) point clouds by using a 3D neural network for individual tree detection. Specifically, a sparse convolutional network was used for 3D feature extraction, feeding both semantic segmentation and circular object detection outputs, which were combined for further increased accuracy. We demonstrate the capability of our approach on an urban topographic ALS point cloud with 10,864 hand-labeled ground truth trees. Our method achieved an average precision of 83% regarding the common 0.5 intersection over union criterion. 85% percent of the stems were found correctly with a precision of 88%, while tree area was covered by the individual tree detections with an F1 accuracy of 92%. Thereby, we outperformed traditional delineation baselines and recent detection networks.

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“…With F1 scores over 97%, we reached the limits of ground truth quality [86]. Similar results can be expected from other point cloud classification networks [84,86,119].…”

Section: Stem Precision and Recallsupporting

confidence: 87%

Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks

2022

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“…The ultimate goal of the contest is to build models to understand the state and changes in the manmade and natural environment using multisensor and multitemporal remote sensing data for sustainable development. This contest was designed as a benchmarking competition following previous editions [1], [2], [4], [6], [7]. The 2021 DFC had two tracks running in parallel: 1) Track DSE: detection of settlements without electricity 2) Track MSD: multitemporal semantic change detection.…”

Section: The 2021 Data Fusion Contestmentioning

confidence: 99%

Report on the 2021 IEEE GRSS Data Fusion Contest—Geospatial Artificial Intelligence for Social Good [Technical Committees]

Yokoya

Ghamisi

Hänsch

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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“…The contest follows the ultimate goal of building models to understand the state and changes of artificial and natural environments using multisensor and multitemporal remote sensing data towards sustainable developments. The contest is designed as a benchmark competition following previous editions [16]- [20].…”

mentioning

confidence: 99%

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

Ma¹,

Li²,

Feng³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part B

Cited by 24 publications

References 30 publications

Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks

Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks

Report on the 2021 IEEE GRSS Data Fusion Contest—Geospatial Artificial Intelligence for Social Good [Technical Committees]

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

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