Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,42 @@
|
|
| 1 |
---
|
| 2 |
license: mit
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
+
pipeline_tag: object-detection
|
| 4 |
+
tags:
|
| 5 |
+
- yolov8
|
| 6 |
+
- remote sensing
|
| 7 |
+
- aerial imagery
|
| 8 |
+
- beaver
|
| 9 |
+
- object detection
|
| 10 |
---
|
| 11 |
+
# This is a yolov8 based object detection model for beaver dams and lodges from aerial imagery
|
| 12 |
+
This is a semi-serious side-project to detect beaver dams and lodges from aerial imagery.
|
| 13 |
+
Beavers are expanding into Arctic regions, which can be even observed indirectly from space.
|
| 14 |
+
With very-high resolution data from UAV or airborne missions, we can try to map dams and lodges directly.
|
| 15 |
+
|
| 16 |
+
#### More cool information on beaver expansion into the Arctic:
|
| 17 |
+
* Tape, K. D., Clark, J. A., Jones, B. M., Kantner, S., Gaglioti, B. V., Grosse, G., & Nitze, I. (2022). Expanding beaver pond distribution in Arctic Alaska, 1949 to 2019. Scientific Reports, 12(1), 7123. https://doi.org/10.1038/s41598-022-09330-6
|
| 18 |
+
* Jones, B. M., Tape, K. D., Clark, J. A., Nitze, I., Grosse, G., & Disbrow, J. (2020). Increase in beaver dams controls surface water and thermokarst dynamics in an Arctic tundra region, Baldwin Peninsula, northwestern Alaska. Environmental Research Letters, 15(7), 075005. https://doi.org/10.1088/1748-9326/ab80f1
|
| 19 |
+
* Tape, K. D., Jones, B. M., Arp, C. D., Nitze, I., & Grosse, G. (2018). Tundra be dammed: Beaver colonization of the Arctic. Global Change Biology, 24(10), 4478–4488. https://doi.org/10.1111/gcb.14332
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
## General Info
|
| 23 |
+
This model takes RGB aerial images in high spatial resolution, suhc as UAV or airborne imagery. It was trained on images from tundra regions in NW Alaska.
|
| 24 |
+
|
| 25 |
+
Target objects were hand labelled with roboflow --> https://app.roboflow.com/awi-response/beaver-finder-vhr-imagery-a9hg9/
|
| 26 |
+
|
| 27 |
+
## Classes
|
| 28 |
+
1: beaver dam
|
| 29 |
+
2: beaver lodge
|
| 30 |
+
3: building (not great)
|
| 31 |
+
## Input data
|
| 32 |
+
|
| 33 |
+
## Examples
|
| 34 |
+
|
| 35 |
+
### The good ones
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
### The bad ones
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
|