Mapping cultural ecosystem services 2.0 – Potential and shortcomings from unlabeled crowd sourced images

Lee, Heera; Seo, Byong Seol; Koellner, Thomas; Lautenbach, Sven

doi:10.1016/j.ecolind.2018.08.035

Cited by 86 publications

(41 citation statements)

References 65 publications

(109 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The functioning and stability of ecosystems can then be monitored by converting all these species data and interactions into food web models and/or focusing on indicator species such as bats, which are very sensitive to habitat and climate changes (Mac Aodha et al, ). And the importance of ecosystem services can be assessed to help decision makers with their policies or management decisions (Lee, Seo, Koellner, & Lautenbach, ).…”

Section: Overview Of Applications In Ecologymentioning

confidence: 99%

Applications for deep learning in ecology

2019

View full text Add to dashboard Cite

A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

show abstract

Section: Overview Of Applications In Ecologymentioning

confidence: 99%

Applications for deep learning in ecology

2019

View full text Add to dashboard Cite

show abstract

“…While this study focused on simply counting social media posts to estimate recreation use, other information included in posts clearly offers opportunities for understanding visitation, recreation behavior, and visitor characteristics. The content (words and images) and other metadata associated with social media (such as the user’s profile) may provide additional insights into visitor numbers, experiences, activities, and satisfaction during the recreation visit 39 , 40 , as well as the characteristics of the visitors themselves, though research is needed in order to understand this potential. Layered on top of these research opportunities is an ongoing need for appropriate protocols to protect individual privacy and maintain ethical standards.…”

Section: Discussionmentioning

confidence: 99%

Next-generation visitation models using social media to estimate recreation on public lands

Wood

Winder

Lia

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

show abstract

“…This technique also identifies distinct characteristics of the area based on the needs of local citizens. Furthermore, combining big data (social networking service data such as Twitter, Facebook, YouTube, and Flickr) [19,20] and regional micro data (in-depth interviews and civil complaints) [21,22] would enable the prioritization of land-use policies specialized for each location. When determining such priorities, spatial text mining could provide important data that represents the needs and opinions of local residents.…”

Section: Discussionmentioning

confidence: 99%

Mapping Environmental Conflicts Using Spatial Text Mining

Lee

Kim

2020

Land

View full text Add to dashboard Cite

Mapping the characteristics and extent of environmental conflicts related to land use is important for developing regionally specific policies. However, because it is only possible to verify the frequency of conflicts on a specific predetermined subject, it is difficult to determine the various reasons for conflicts in a region. Therefore, this study mapped the current status of regional environmental conflicts in South Korea using a spatial text mining technique, then proposed relevant management policies. The results were obtained by analyzing environmental conflict data extracted from the online agendas of regional environmental organizations. Air quality-related conflicts in South Korea are concentrated in western municipalities; development-related conflicts are concentrated in the southern region of Jeju Island; and intensive safety-related conflicts occur in metropolitan areas, particularly Ulsan. Thus, the type of conflict is determined by the local environment, in accordance with the definition of environmental conflict, and the distribution is determined by the location of the stakeholder population. This study reveals the issues and locations related to local environmental conflict that require further attention, and proposes more wide-ranging methods for managing the links between conflicts by mapping environmental conflicts on a large scale rather than on an individual basis.

show abstract

Mapping cultural ecosystem services 2.0 – Potential and shortcomings from unlabeled crowd sourced images

Cited by 86 publications

References 65 publications

Applications for deep learning in ecology

Applications for deep learning in ecology

Next-generation visitation models using social media to estimate recreation on public lands

Mapping Environmental Conflicts Using Spatial Text Mining

Contact Info

Product

Resources

About