Rankings
Leaderboards below indicate the rankings of the accepted papers taken from Codalab. We would like to thank all participants for hundreds of submissions and papers submitted for DeepGlobe. We are planning to re-open the competitions and pursue DeepGlobe as a benchmark after CVPR.
Road Extraction Challenge
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Building Detection Challenge
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Land Cover Classification Challenge
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Posters
Road Extraction |
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1. Semantic Binary Segmentation using Convolutional Networks without Decoders
Shubhra Aich*, University of Saskatchewan; William van der Kamp, University of Saskatchewan; Ian Stavness, University of Saskatchewan
2. Stacked U-Nets with Multi-Output for Road Extraction
Tao Sun*, Tongji University; Zehui Chen, Tongji University; Wenxiang Yang, Tongji University; Yin Wang, Tongji University
3. D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Lichen Zhou*, Beijing University of Posts and Telecommunications; Chuang Zhang, Beijing University of Posts and Telecommunications; Ming Wu, Beijing University of Posts and Telecommunications
4. Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
Alexander Buslaev*, Mapbox; Selim Seferbekov, Veeva Systems; Vladimir Iglovikov, Lyft Inc; Alexey Shvets
Massachusetts Institute of Technology
5. Road Detection with EOSResUNet and Post Vectorizing Algorithm
Oleksandr Filin*, EOS Data Analytics; Serhii Panchenko, EOS Data Analytics; Anton Zapara, EOS Data Analytics
6. Residual Inception Skip Network for Binary Segmentation
Jigar Doshi*, CrowdAI
7. Roadmap Generation using a Multi-Stage Ensemble of Neural Networks with Smoothing-Based Optimization
Dragos Costea*, University Politehnica of Bucharest; Alina Marcu, University Politehnica of Bucharest; Emil Slusanschi, University Politehnica of Bucharest; Marius Leordeanu, University Politehnica of Bucharest
Shubhra Aich*, University of Saskatchewan; William van der Kamp, University of Saskatchewan; Ian Stavness, University of Saskatchewan
2. Stacked U-Nets with Multi-Output for Road Extraction
Tao Sun*, Tongji University; Zehui Chen, Tongji University; Wenxiang Yang, Tongji University; Yin Wang, Tongji University
3. D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Lichen Zhou*, Beijing University of Posts and Telecommunications; Chuang Zhang, Beijing University of Posts and Telecommunications; Ming Wu, Beijing University of Posts and Telecommunications
4. Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
Alexander Buslaev*, Mapbox; Selim Seferbekov, Veeva Systems; Vladimir Iglovikov, Lyft Inc; Alexey Shvets
Massachusetts Institute of Technology
5. Road Detection with EOSResUNet and Post Vectorizing Algorithm
Oleksandr Filin*, EOS Data Analytics; Serhii Panchenko, EOS Data Analytics; Anton Zapara, EOS Data Analytics
6. Residual Inception Skip Network for Binary Segmentation
Jigar Doshi*, CrowdAI
7. Roadmap Generation using a Multi-Stage Ensemble of Neural Networks with Smoothing-Based Optimization
Dragos Costea*, University Politehnica of Bucharest; Alina Marcu, University Politehnica of Bucharest; Emil Slusanschi, University Politehnica of Bucharest; Marius Leordeanu, University Politehnica of Bucharest
Building Detection |
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8. Rotated Rectangles for Symbolized Building Footprint Extraction
Matthew Dickenson*, Uber; Lionel Gueguen, Uber
9. Building Detection from Satellite Imagery Using Composite Loss Function
Sergey Golovanov*, Neuromation; Rauf Kurbanov, Neuromation; Aleksey Artamonov, Neuromation; Alex Davydow, Neuromation; Sergey Nikolenko, Neuromation
10. Building Detection from Satellite Imagery using Ensemble of Size-specific Detectors
Ryuhei Hamaguchi*, Pasco Corporation; Shuhei Hikosaka, Pasco Corporation
11. TernausNetV2: Fully Convolutional Network for Instance Segmentation
Vladimir Iglovikov*, Lyft Inc; Selim Seferbekov, Veeva Systems; Alexander Buslaev, Mapbox; Alexey Shvets
Massachusetts Institute of Technology
12. Semantic Segmentation based Building Extraction Method using Multi-source GIS Map Datasets and Satellite Imagery
Weijia Li*, Tsinghua University; Conghui He, Tsinghua University; Jiarui Fang, Tsinghua University ; Haohuan Fu,
13. CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge
Remi Delassus*, Qucit - LaBRI; Romain Giot, Univ. Bordeaux
14. Building Extraction from Satellite Images Using Mask R-CNN with Building Boundary Regularization
Kang Zhao*, York University; Jungwon Kang, York University; Jaewook Jung, Thales Canada ; Gunho Sohn, York University
Matthew Dickenson*, Uber; Lionel Gueguen, Uber
9. Building Detection from Satellite Imagery Using Composite Loss Function
Sergey Golovanov*, Neuromation; Rauf Kurbanov, Neuromation; Aleksey Artamonov, Neuromation; Alex Davydow, Neuromation; Sergey Nikolenko, Neuromation
10. Building Detection from Satellite Imagery using Ensemble of Size-specific Detectors
Ryuhei Hamaguchi*, Pasco Corporation; Shuhei Hikosaka, Pasco Corporation
11. TernausNetV2: Fully Convolutional Network for Instance Segmentation
Vladimir Iglovikov*, Lyft Inc; Selim Seferbekov, Veeva Systems; Alexander Buslaev, Mapbox; Alexey Shvets
Massachusetts Institute of Technology
12. Semantic Segmentation based Building Extraction Method using Multi-source GIS Map Datasets and Satellite Imagery
Weijia Li*, Tsinghua University; Conghui He, Tsinghua University; Jiarui Fang, Tsinghua University ; Haohuan Fu,
13. CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge
Remi Delassus*, Qucit - LaBRI; Romain Giot, Univ. Bordeaux
14. Building Extraction from Satellite Images Using Mask R-CNN with Building Boundary Regularization
Kang Zhao*, York University; Jungwon Kang, York University; Jaewook Jung, Thales Canada ; Gunho Sohn, York University
Land Cover Classification |
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15. Deep Aggregation Net for Land Cover Classification
Tzu-Sheng Kuo*, National Taiwan University; Keng-Sen Tseng, National Taiwan University; Jia-Wei Yan, National Taiwan University; Yen-Cheng Liu, National Taiwan University ; Yu-Chiang Frank Wang, National Taiwan University
16. Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery
Arthita Ghosh*, University of Maryland; Max Ehrlich, University of Maryland; Sohil Shah, University of Maryland, College Park; Larry Davis, University of Maryland; Rama Chellappa, University of Maryland
17. Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss
Alexander Rakhlin*, Neuromation OU; Alex Davydow, Neuromation; Sergey Nikolenko, Neuromation
18. Dense Fusion Classmate Network for Land Cover Classification
Chao Tian*, Harbin Institute of Technology; Cong Li, Sensetime Group Limited; Jianping Shi, Sensetime Group Limited
19. NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation
Mohamed Samy, Nile University; Karim Amer*, Nile University; Kareem Eissa, Nile University; Mahmoud Shaker, Nile University; Mohamed ElHelw, Nile University;
20. Feature Pyramid Network for Multi-Class Land Segmentation
Selim Seferbekov*, Veeva Systems; Vladimir Iglovikov, Lyft Inc; Alexander Buslaev, Mapbox; Alexey Shvets
Massachusetts Institute of Technology
21. Uncertainty Gated Network for Land Cover Segmentation
Guillem Pascual*, Universitat de Barcelona; Santi Seguí, Universitat de Barcelona; Jordi Vitria, Universitat de Barcelona
22. Land Cover Classification With Superpixels and Jaccard Index Post-Optimization
Alex Davydow*, Neuromation; Sergey Nikolenko, Neuromation
Tzu-Sheng Kuo*, National Taiwan University; Keng-Sen Tseng, National Taiwan University; Jia-Wei Yan, National Taiwan University; Yen-Cheng Liu, National Taiwan University ; Yu-Chiang Frank Wang, National Taiwan University
16. Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery
Arthita Ghosh*, University of Maryland; Max Ehrlich, University of Maryland; Sohil Shah, University of Maryland, College Park; Larry Davis, University of Maryland; Rama Chellappa, University of Maryland
17. Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss
Alexander Rakhlin*, Neuromation OU; Alex Davydow, Neuromation; Sergey Nikolenko, Neuromation
18. Dense Fusion Classmate Network for Land Cover Classification
Chao Tian*, Harbin Institute of Technology; Cong Li, Sensetime Group Limited; Jianping Shi, Sensetime Group Limited
19. NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation
Mohamed Samy, Nile University; Karim Amer*, Nile University; Kareem Eissa, Nile University; Mahmoud Shaker, Nile University; Mohamed ElHelw, Nile University;
20. Feature Pyramid Network for Multi-Class Land Segmentation
Selim Seferbekov*, Veeva Systems; Vladimir Iglovikov, Lyft Inc; Alexander Buslaev, Mapbox; Alexey Shvets
Massachusetts Institute of Technology
21. Uncertainty Gated Network for Land Cover Segmentation
Guillem Pascual*, Universitat de Barcelona; Santi Seguí, Universitat de Barcelona; Jordi Vitria, Universitat de Barcelona
22. Land Cover Classification With Superpixels and Jaccard Index Post-Optimization
Alex Davydow*, Neuromation; Sergey Nikolenko, Neuromation