Asset Details
Do you wish to reserve the book?
ForestSplat: Proof-of-Concept for a Scalable and High-Fidelity Forestry Mapping Tool Using 3D Gaussian Splatting
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
ForestSplat: Proof-of-Concept for a Scalable and High-Fidelity Forestry Mapping Tool Using 3D Gaussian Splatting
Do you wish to request the book?
ForestSplat: Proof-of-Concept for a Scalable and High-Fidelity Forestry Mapping Tool Using 3D Gaussian Splatting
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
ForestSplat: Proof-of-Concept for a Scalable and High-Fidelity Forestry Mapping Tool Using 3D Gaussian Splatting
2025
Overview
Accurate, scalable forestry insights are critical for implementing carbon credit-based reforestation initiatives and data-driven ecosystem management. However, existing forest quantification methods face significant challenges: hand measurement is labor-intensive, time-consuming, and difficult to trust; satellite imagery is not accurate enough; and airborne LiDAR remains prohibitively expensive at scale. In this work, we introduce ForestSplat: an accurate and scalable reforestation monitoring, reporting, and verification (MRV) system built from consumer-grade drone footage and 3D Gaussian Splatting. To evaluate the performance of our approach, we map and reconstruct a 200-acre mangrove restoration project in the Jobos Bay National Estuarine Research Reserve. ForestSplat produces an average mean absolute error (MAE) of 0.17 m and mean error (ME) of 0.007 m compared to canopy height maps derived from airborne LiDAR scans, using 100× cheaper hardware. We hope that our proposed framework can support the advancement of accurate and scalable forestry modeling with consumer-grade drones and computer vision, facilitating a new gold standard for reforestation MRV.
