DSCI215
INTRO DEEP LEARNING EARTH OBS
Offered in a current or upcoming term. Official schedule
Other titles (1)
- APPLY'G DEEP LEARN'G EARTH OBS
- Department
- Data Science
- Terms offered
- 5
- Data refreshed
- Jun 27, 2026
Description
Focusing on convolutional networks, this course provides an introduction to how these key machine learning algorithms work and how to apply them to satellite imagery (especially for the task of semantic segmentation), including the full workflow required to acquire and process imagery, develop and train a model, and make and critically evaluate the resulting maps. The course will be strongly hands-on, and emphasize the use of agents for developing python-based modeling pipelines within open and reproducible workflows. For DSCI students, this course satisfies the capstone requirement.
Syllabi
Sign in to view syllabi.
Offerings and forecast 2023–2026
2023 2026
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: not enough history to forecast. | 2030 Spring: 64% chance, low confidence. Offered in every spring term since 2023. Department course count rising about 16% per year. Department faculty is about 55% above its recent average. | 2030 Summer: 7% chance, medium confidence. Offered in 0 of 9 summer terms since 2018. | 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020. |
| 2029 | 2029 Fall: not enough history to forecast. | 2029 Spring: 68% chance, low confidence. Offered in every spring term since 2023. Department course count rising about 22% per year. Department faculty is about 69% above its recent average. | 2029 Summer: 6% chance, medium confidence. Offered in 0 of 10 summer terms since 2017. | 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2028 | 2028 Fall: not enough history to forecast. | 2028 Spring: 72% chance, low confidence. Offered in every spring term since 2023. Department course count rising about 21% per year. Department faculty is about 76% above its recent average. | 2028 Summer: 5% chance, medium confidence. Offered in 0 of 11 summer terms since 2016. | 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2027 | 2027 Fall: not enough history to forecast. | 2027 Spring: 75% chance, low confidence. Offered in every spring term since 2023. Department course count rising about 21% per year. Department faculty is about 76% above its recent average. | 2027 Summer: 5% chance, high confidence. Offered in 0 of 12 summer terms since 2015. | 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2026 | 1 section | 1 section | Not offered | No data |
| 2025 | Not offered | 1 section | Not offered | Not offered |
| 2024 | Not offered | 1 section | Not offered | Not offered |
| 2023 | Not offered | 1 section | Not offered | Not offered |
Forecast Under 15% 15–39% 40–69% 70–89% 90%+ Not enough history
Fall Spring Summer Intersession More sections Not offered No data
Meeting times
Recent first- Mon, Wed, 4:15 PM–5:30 PM JC215 2026-22026-22025-1
- Fri, 2:50 PM–4:05 PM JC215 2026-12026-1
- Thu, 4:15 PM–5:30 PM MACD002 2025-2
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Fall 2026 | Lyndon D. Estes | Lecture | 1 | 6 of 30 enrolled | 24788 |
| Spring 2026 | Lyndon D. Estes | Lecture | 1 | 17 of 30 enrolled | 34044 |
| Spring 2025 | Lyndon D. Estes | Lecture | 1 | 3 of 15 enrolled | 34044 |
| Spring 2024 | Lyndon D. Estes | Lecture | 1 | 0 of 15 enrolled | 32502 |
| Spring 2023 | Kenneth J. Basye | Lecture | 1 | 6 of 15 enrolled | 32502 |