If you made any changes in Pure these will be visible here soon.

Fingerprint Dive into the research topics where Shivangi Srivastava is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 7 Similar Researchers
learning Earth & Environmental Sciences
Neural networks Engineering & Materials Science
Labels Engineering & Materials Science
Land use Engineering & Materials Science
Photointerpretation Engineering & Materials Science
Information use Engineering & Materials Science
imagery Earth & Environmental Sciences
Multilayer neural networks Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2018 2019

  • 2 Paper
  • 2 Article
  • 1 Conference contribution
1 Citation (Scopus)

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Srivastava, S., Vargas-Muñoz, J. E. & Tuia, D., Jul 2019, In : Remote Sensing of Environment. 228, p. 129-143

Research output: Contribution to journalArticleAcademicpeer-review

land use
urban planning
Urban planning

Deep learning based methods for building segmentation from remote sensing data

Lobry, S., Marcos Gonzalez, D., Vargas Munoz, J., Kellenberger, B. A., Srivastava, S. & Tuia, D., 2018. 4 p.

Research output: Contribution to conferencePaperAcademic

2 Citations (Scopus)
Open Access
search engine
visual cue

Land-use characterisation using Google Street View pictures and OpenStreetMap

Srivastava, S., Lobry, S., Tuia, D. & Vargas Munoz, J., 2018. 5 p.

Research output: Contribution to conferencePaperAcademic

Open Access
Land use
Information use
Multilayer neural networks
Support vector machines
1 Citation (Scopus)

Multi-label building functions classification from ground pictures using convolutional neural networks

Srivastava, S., Vargas Muñoz, J. E., Swinkels, D. & Tuia, D., 6 Nov 2018, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. New York: ACM, p. 43-46

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Neural networks
Electric fuses

Projects 2017 2017