A data science challenge for converting airborne remote sensing data into ecological information

PeerJ

article

Sergio Marconi¹  Sarah J. Graves²  Dihong Gong³  Morteza Shahriari Nia³  Marion Le Bras⁴  Bonnie J. Dorr⁴  Peter Fontana⁴  Justin Gearhart¹  Craig Greenberg⁴  Dave J. Harris⁵  Sugumar Arvind Kumar³  Agarwal Nishant³  Joshi Prarabdh³  Sundeep U. Rege³  Stephanie Ann Bohlman²  Ethan P. White⁵  Daisy Zhe Wang³ 

[1] School of Natural Resources and Environment, University of Florida;School of Forest Resources and Conservation, University of Florida;Department of Computer and Information Science and Engineering, University of Florida;National Institute of Standards and Technology;Department of Wildlife Ecology and Conservation, University of Florida

关键词: Airborne remote sensing;    Species classification;    Remote sensing;    Data alignment;    National Ecological Observatory Network;    Data science competition;    Crown segmentation;    Crown delineation;   

DOI  :  10.7717/peerj.5843

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: (1) crown segmentation, for identifying the location and size of individual trees; (2) alignment, to match ground truthed trees with remote sensing; and (3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks. The crown segmentation task proved to be the most challenging, with the highest-performing algorithm yielding only 34% overlap between remotely sensed crowns and the ground truthed trees. However, most algorithms performed better on large trees. For the alignment task, an algorithm based on minimizing the difference, in terms of both position and tree size, between ground truthed and remotely sensed crowns yielded a perfect alignment. In hindsight, this task was over simplified by only including targeted trees instead of all possible remotely sensed crowns. Several algorithms performed well for species classification, with the highest-performing algorithm correctly classifying 92% of individuals and performing well on both common and rare species. Comparisons of results across algorithms provided a number of insights for improving the overall accuracy in extracting ecological information from remote sensing. Our experience suggests that this kind of competition can benefit methods development in ecology and biology more broadly.

【 授权许可】

CC BY