Fusing LandTrendr BCI and machine learning for spoil dump mapping
Xu Zhen, Zhijian Yu, YuMing Shi, Yiyuan Zhao · 2025
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Description
Xu Zhen and his three co-authors, publishing in 2025, build an automated way to map waste piles at open-pit mines. Spoil dumps change fast as fresh rock is dumped, then slowly as plants reclaim them. Telling the two stages apart from satellites is hard. The team fuses four decades of Landsat images with a new index that exposes bare coal, plus an algorithm that tracks vegetation disturbance and recovery over time.
A machine learning classifier combines those signals with terrain data. It sorts dumps into internal and external types and traces their edges. The coal index hit 92 percent accuracy, well above standard indices. The full system mapped dump types at 86 percent accuracy, a fifth better than disturbance tracking alone. The authors offer the framework for long-term monitoring of mining landscapes.
Catalog record
| CREATOR | Xu Zhen, Zhijian Yu, YuMing Shi, Yiyuan Zhao |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |