Pub Date : 2021-08-31DOI: 10.1093/oso/9780198850243.003.0001
A. Piel, S. Wich
For decades, conservation has lagged behind the rate and scale of some of the world’s primary environmental challenges, with scientists unable to collect, monitor, and incorporate sufficient data necessary to support addressing global threats to wildlife and their habitat. However, with innovative technology, we are rapidly improving the way that scientists can provide data for decision-makers. We can now monitor key ecosystem components in near real-time, remotely revealing changes from the scale of individual trees up to entire forest blocs. Data collectors use smartphones to identify and report illegal human activity such as poaching and logging, relaying information to critical stakeholders. Finally, computer scientists are developing algorithms to more efficiently process incoming large data sets, minimizing turnaround time from data collection to taking preventive steps for species conservation. In some cases, the speed of technological solutions has outpaced the ethical guidelines to limit their use, especially when resulting data may infringe on people’s privacy. Regardless, the progress has thrust technological solutions for biological problems to the forefront of conservation. The threats to biodiversity show little sign of abating, but technology is narrowing the gap between the tempo and scale of the problem, and our understanding of how to develop solutions.
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Pub Date : 2021-08-31DOI: 10.1093/oso/9780198850243.003.0011
D. Morris, L. Joppa
Computer vision (CV) is rapidly advancing as a tool to make conservation science more efficient, for example, by accelerating the annotation of images from camera traps and aerial surveys. However, before CV can become a widely used approach, several core technology challenges need to be addressed by the CV community. Taking into consideration several case studies in CV where tremendous progress has been made since the emergence of deep learning, this chapter will introduce core concepts in CV, survey several areas where CV is already contributing to conservation, and outline key challenges for the CV community that will facilitate the adoption of CV in mainstream conservation practice.
{"title":"Challenges for the computer vision community","authors":"D. Morris, L. Joppa","doi":"10.1093/oso/9780198850243.003.0011","DOIUrl":"https://doi.org/10.1093/oso/9780198850243.003.0011","url":null,"abstract":"Computer vision (CV) is rapidly advancing as a tool to make conservation science more efficient, for example, by accelerating the annotation of images from camera traps and aerial surveys. However, before CV can become a widely used approach, several core technology challenges need to be addressed by the CV community. Taking into consideration several case studies in CV where tremendous progress has been made since the emergence of deep learning, this chapter will introduce core concepts in CV, survey several areas where CV is already contributing to conservation, and outline key challenges for the CV community that will facilitate the adoption of CV in mainstream conservation practice.","PeriodicalId":158957,"journal":{"name":"Conservation Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132492169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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