Contextual classification of lidar data and building object detection in urban areas

By Joachim Niemeyer, Franz Rottensteiner, Uwe Soergel. 2014

569 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ecology, Environmental Engineering, Geology, Remote Sensing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Raquel Urtasun
2. Xiaogang Wang
3. Luc Van Gool
4. Sanja Fidler
5. Dacheng Tao
6. Ming-Hsuan Yang
7. Binh Yang
8. Alan L. Yuille

In Ecology

1. Loic Landrieu
2. Martin Simonovsky
3. Hugo Ledoux
4. François Goulette
5. Jean-Emmanuel Deschaud
6. Xavier Roynard
7. Huapeng Li
8. Xin Pan

In Environmental Engineering

1. Hugo Ledoux
2. Liangliang Nan
3. Jan Dirk Wegner
4. B. Boom
5. Weixiao Gao
6. Jean-Emmanuel Deschaud
7. François Goulette
8. Xavier Roynard

In Geology

1. Hugo Ledoux
2. Liangliang Nan
3. B. Boom
4. Weixiao Gao
5. Jean-Emmanuel Deschaud
6. François Goulette
7. Xavier Roynard
8. Loic Landrieu

In Remote Sensing

1. Loic Landrieu
2. Martin Simonovsky
3. Hugo Ledoux
4. Liangliang Nan
5. François Goulette
6. Jean-Emmanuel Deschaud
7. Xavier Roynard
8. Raquel Urtasun

Citations in the corpus

• 3D Instance Segmentation of MVS Buildings (2021, background): “Most of existing 3-D datasets are designed for semantic segmentation, such as Vaihingen3D [53], Swiss3DCities [54], Hessigheim3D [55], and Semantic Urban Meshes (SUM) [56].”
• The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo (2021, methodology): “One prominent example of the latter case is the Vaihingen 3D (V3D) dataset acquired by Cramer (2010), which served as the basis for the ISPRS 3D Semantic Labeling bench-4 mark (Niemeyer et al., 2014).”
• Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery (2020, methodology): “[25] Niemeyer J, Rottensteiner F, Soergel U.”
• Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs (2017, methodology): “The solution is then spatially regularized using graphical models [37, 24, 34, 44, 21, 2, 38, 35, 49] or structured optimization [27].”
• SUM: A Benchmark Dataset of Semantic Urban Meshes (2021, methodology): “As for ALS benchmark point clouds, representative datasets are ISPRS [22], DublinCity [23], and LASDU [25] covering various scales of city landscapes and were annotated manually with off-the-shelf software.”
• Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification (2017, methodology): “Closer to our field of research are airborne laser scanning (ALS) datasets as provided with the 3D Semantic Labeling Contest10 (Niemeyer et al., 2014).”
• TorontoCity: Seeing the World with a Million Eyes (2016, background): “The photometry community has developed several benchmarks towards urban scene understanding [16, 25, 31, 26, 21].”
• An object-based convolutional neural network (OCNN) for urban land use classification (2018, methodology): “Four multispectral bands (Red, Green, Blue, 219 and Near Infrared) together with a digital surface model (DSM), useful for differentiating urban 220 objects with height information (Niemeyer et al., 2014), were incorporated as multiple input 221 data sources for the image segmentation (Figure 1(A)).”