ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

By P. Rönnholm, Xinlian Liang, A. Kukko, Anttoni Jaakkola, Juha Hyyppä. 2016

495 citations recorded by the source corpus.

The work of ISPRS Technical Commission VII is devoted to Thematic Processing, Modeling and Analysis of Remotely Sensed Data. This volume collects the papers which have been selected by the Technical Commission committee consisting of the President, Vice ‐ President and Working Group Chairs. The papers have been subject to a double blind, peer review process by two or three reviewers. The reviewers were asked to evaluate the papers as they were submitted according to content, significance, originality, relevance of the paper

Subfields: Environmental Engineering, Geology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/6e8ca9859ae2e774734ab8a5632d101ca9708604

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Citations in the corpus

SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds (2015, methodology): “[47] may be preferable to that used in SimpleTree, as it does not require manual interaction.”
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors (2018, unknown): “Initial attempts to learn from 3D data used the naive dense voxel grid representation [51, 46, 26, 11].”
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning (2017, methodology): “…have used DCNNs pre-trained on ImageNet to yield state-of-the-art results for the semantic segmentation of highresolution multispectral aerial imagery (Wang et al., 2017; Marmanis et al., 2016) because the most widely used benchmarks (Rottensteiner et al., 2012) only use a single non-RGB band.”
SEN12MS - A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion (2019, methodology): “While the procedure is described in detail in (Schmitt et al., 2019), a short summary of the three modules is as follows:”
Attention Concatenation Volume for Accurate and Efficient Stereo Matching (2022, methodology): “Based on the advantages of the proposed ACV, we design an accurate stereo matching network ACVNet, which ranks the 2 on the KITTI 2012 [5] and KITTI 2015 [13] benchmark, the 2 on Scene Flow [12], and the 3 on the ETH3D [15] benchmark among all the published methods (see Figure 1).”
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): “For the geometric reconstruction of the scene, both LiDAR data and imagery were used to beneﬁt from their complementary properties (Mandlburger et al., 2017).”