The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning to Detect Roads in High-Resolution Aerial Images
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Citations
Cites
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 4×
“We train an RBM on the PCA representations of aerial image patches by approximately minimizing Contrastive Divergence using stochastic gradient descent with momentum.”
BackgroundMethodInfluentialSelf-citation
Cited by
Road Extraction by Deep Residual U-Net
Zhang, Z., Liu, Q., & Wang, Y. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2802944 · Cited by 3,179
“To demonstrate the accuracy and efficiency of the proposed deep ResUnet, we test it on Massachusetts roads data set2 and compare it with three state of the art methods, including Mnih’s [2] method, Saito’s method [5], and U-Net [24].”
BackgroundMethodResultInfluentialContext-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Combining satellite imagery and machine learning to predict poverty
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016) · Science · doi:10.1126/science.aaf7894 · Cited by 1,664
Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.191 · Cited by 196
“The PR curve is a popular performance metric for object detection in aerial imagery [32,38,71,72], and therefore it is adopted here.”
BackgroundMethodCountry-wide high-resolution vegetation height mapping with Sentinel-2
Lang, N., Schindler, K., & Wegner, J. D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111347 · Cited by 202
Merkle, N., Luo, W., Auer, S., Müller, R., & Urtasun, R. (2017) · Remote Sensing · doi:10.3390/rs9060586 · Cited by 120
Liu, T., & Abd-Elrahman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.006 · Cited by 103
Object Detection from the Video Taken by Drone via Convolutional Neural Networks
Sun, C., Zhan, W., She, J., & Zhang, Y. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/4013647 · Cited by 40
Forest point processes for the automatic extraction of networks in raster data
Schmidt, A., Lafarge, F., Brenner, C., Rottensteiner, F., & Heipke, C. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.01.012 · Cited by 21
Metadata
- Authors
- Mnih, V., & Hinton, G. E.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 210-223
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W73112891
- Cited by
- 624
- References
- 1
- FWCI
- 18.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
