Xiaowen Ma

Papers

Total Citations

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About

Xiaowen Ma is a researcher whose work centers on the intersection of precision forestry, sensor technology, and deep learning. Her primary research area involves developing automated, cost-effective methods for forest resource monitoring, with a particular focus on tree stem diameter (SD) measurement—a critical but traditionally labor-intensive task. In her most cited work, Ma introduced a novel device combining an image sensor with a laser module, coupled with a deep learning algorithm, to automate SD measurement. This innovation directly addresses the need for rapid, non-expert field data collection, reducing both time and cost while maintaining accuracy. Although her work is early-stage, with her top-cited paper accumulating 4 citations, it represents a foundational step toward scalable, AI-driven forest inventory. Ma’s contributions are notable for their practical engineering approach, merging computer vision with ecological field methods. Her research holds significant promise for advancing sustainable forest management and remote sensing applications, positioning her as an emerging voice in automated environmental monitoring.