IDTreeS
Understanding and managing forests is crucial to understanding and potentially mitigating the effects of climate change, invasive species, and shifting land use on natural systems and human society. However, collecting data on individual trees in the field is expensive and time consuming, which limits the scales at which this crucial data is collected. Combining large scale survey efforts with remotely sensed imagery from satellites, airplanes, and drones provide the potential to observe ecosystems at much larger scales than using field data alone.
Dr. Sergio Marconi (He/Him)
Post-Doc at University of Florida
My research focuses on AI for forest cross-scale ecology and conservation
Post
Global monitoring for Nature conservation
My upcoming blog post about opportunities of global monitoring with upcoming satellite missions
Pubblicazioni
NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns
Forests provide essential biodiversity, ecosystem and economic services. Information on individual trees is important for understanding the state of forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs …
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features of individuals that affect their fitness and performance). Analyzing trait distributions within and among forests could …
DeepForest: A Python package for RGB deep learning tree crown delineation
Remote sensing of forested landscapes can transform the speed, scale and cost of forest research. The delineation of individual trees in remote sensing images is an essential task in forest analysis. Here we introduce a new Python package, DeepForest …
Cross-site learning in deep learning RGB tree crown detection
Tree crown detection is a fundamental task in remote sensing for forestry and ecosystem ecology. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with …
Rethinking global carbon storage potential of trees. A comment on Bastin et al.(2019)
Bastin et al. 2019 use two flawed assumptions: 1) that the area suitable for restoration does not contain any carbon currently, and 2) that soil organic carbon (SOC) from increased canopy cover will accumulate quickly enough to mitigate anthropogenic …
Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks
Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying …