Machine learning for satellite imagery when labels are scarce
Abstract/Contents
- Abstract
- As the world aims to achieve the UN Sustainable Development Goals by 2030, data gaps in the developing world make it difficult to measure progress and target interventions. Recent rapid advances in computer vision and satellite imagery acquisition offer opportunities for automatically extracting knowledge about our planet from space. Compared to field surveys, which are the traditional source of knowledge about human activities and natural ecosystems, satellites offer global coverage at low marginal cost. However, many regions of interest around the world lack ground truth labels on which to train machine learning models. This dissertation will cover two strategies for mapping our planet when labels are scarce: (1) learning better features on satellite imagery to maximize label use efficiency and (2) using non-traditional data sets as ground truth. The application area of focus is agriculture, with a particular focus on cropland and crop type mapping, but the methods and data modes considered can be applied to any domain that uses remotely sensed data.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wang, Sherrie |
|---|---|
| Degree supervisor | Lobell, David |
| Thesis advisor | Lobell, David |
| Thesis advisor | Ermon, Stefano |
| Thesis advisor | Gerritsen, Margot (Margot G.) |
| Degree committee member | Ermon, Stefano |
| Degree committee member | Gerritsen, Margot (Margot G.) |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sherrie Wang. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/zw179sw4070 |
Access conditions
- Copyright
- © 2021 by Sherrie Wang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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