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Graves SJ, Marconi S, Stewart D, Harmon I, Weinstein B, Kanazawa Y, Scholl VM, Joseph MB, McGlinchy J, Browne L, Sullivan MK, Estrada-Villegas S, Wang DZ, Singh A, Bohlman S, Zare A, White EP. Data science competition for cross-site individual tree species identification from airborne remote sensing data. PeerJ 2023; 11:e16578. [PMID: 38144190 PMCID: PMC10749090 DOI: 10.7717/peerj.16578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods' ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46-0.55, macro F1 = 0.09-0.32, cross entropy loss = 2.4-9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07-0.32, macro F1 = 0.02-0.18, cross entropy loss = 2.8-16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.
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Affiliation(s)
- Sarah J. Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Sergio Marconi
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States
| | - Dylan Stewart
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States
| | - Ira Harmon
- Department of Computer and Information Sciences and Engineering, University of Florida, Gainesville, Florida, United States
| | - Ben Weinstein
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States
| | - Yuzi Kanazawa
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., Kawasaki, Kanagawa, Japan
| | - Victoria M. Scholl
- Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, Colorado, United States
- Department of Geography, University of Colorado at Boulder, Boulder, Colorado, United States
| | - Maxwell B. Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, Colorado, United States
| | - Joseph McGlinchy
- Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, Colorado, United States
| | - Luke Browne
- Yale School of the Environment, Yale University, New Haven, Connecticut, United States
| | - Megan K. Sullivan
- Yale School of the Environment, Yale University, New Haven, Connecticut, United States
| | | | - Daisy Zhe Wang
- Department of Computer and Information Sciences and Engineering, University of Florida, Gainesville, Florida, United States
| | - Aditya Singh
- Department of Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States
| | - Stephanie Bohlman
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States
- Informatics Institute, University of Florida, Gainesville, Florida, United States
- Biodiversity Institute, University of Florida, Gainesville, Florida, United States
| | - Ethan P. White
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States
- Informatics Institute, University of Florida, Gainesville, Florida, United States
- Biodiversity Institute, University of Florida, Gainesville, Florida, United States
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Zhou P, Wang N, Zhao S, Zhang Y, Wu X. Difficult Novel Class Detection in Semisupervised Streaming Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:6872-6886. [PMID: 36279327 DOI: 10.1109/tnnls.2022.3213682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Streaming data mining can be applied in many practical applications, such as social media, market analysis, and sensor networks. Most previous efforts assume that all training instances except for the novel class have been completely labeled for novel class detection in streaming data. However, a more realistic situation is that only a few instances in the data stream are labeled. In addition, most existing algorithms are potentially dependent on the strong cohesion between known classes or the greater separation between novel class and known classes in the feature space. Unfortunately, this potential dependence is usually not an inherent characteristic of streaming data. Therefore, to classify data streams and detect novel classes, the proposed algorithm should satisfy: 1) it can handle any degree of separation between novel class and known classes (both easy and difficult novel class detection) and 2) it can use limited labeled instances to build algorithm models. In this article, we tackle these issues by a new framework called semisupervised streaming learning for difficult novel class detection (SSLDN), which consists of three major components: an effective novel class detector based on random trees, a classifier by using the information of nearest neighbors, and an efficient updating process. Empirical studies on several datasets validate that SSLDN can accurately handle different degrees of separation between the novel and known classes in semisupervised streaming data.
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