논문

2018.09.25 22:18 from 분류없음

플리커 사진으로 생태계 서비스 매핑하고 연결망 분석한 논문 나왔습니다. 


요새 한 반 년은 뭘 했나 돌아보면, CNN이랑 DQN 한다고 모 많이 하고 있고 (텐서플로우로 구현하고 우리 기존에 가지고 있는 생태계 모델들이랑 엮는 작업), NVIDIA GPU Grant 신청해서 받았고, 원격탐사 학회로 스페인 한 번 다녀왔고 어.. EGU 올해 다녀왔고, 다시 사회과학 공부를 좀 해야 해서 제도 경제학 책 읽고 그러는 중입니다. 이탄습지 연구도 참여해서 필드했고, 여러가지 여튼 최대한 ML/AI를 써서 생태계 연구, 토지 이용 연구에 붙여 보려고 발버둥.. 계약 기간 3년 중에 벌써 1년이 갔네요 그러다 보니. 


https://www.sciencedirect.com/science/article/pii/S1470160X1830637X

https://doi.org/10.1016/j.ecolind.2018.08.035  


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

Highlights

We introduce an approach to a content analysis of geotagged photos for CES uses.

By using automated tags and a network analysis, themes of the photos were grouped.

This method allowed to distinguish CES- and non-CES-related photos.

This approach can provide spatial information about socio-cultural uses.

Our approach is applicable for crowd-sourced photos available in other regions.

Abstract

The volume of accessible geotagged crowdsourced photos has increased. Such data include spatial, temporal, and thematic information on recreation and outdoor activities, thus can be used to quantify the demand for cultural ecosystem services (CES). So far photo content has been analyzed based on user-labeled tags or the manual labeling of photos. Both approaches are challenged with respect to consistency and cost-efficiency, especially for large-scale studies with an enormous volume of photos. In this study, we aim at developing a new method to analyze the content of large volumes of photos and to derive indicators of socio-cultural usage of landscapes. The method uses machine-learning and network analysis to identify clusters of photo content that can be used as an indicator of cultural services provided by landscapes. The approach was applied in the Mulde river basin in Saxony, Germany. All public Flickr photos (n = 12,635) belonging to the basin were tagged by deep convolutional neural networks through a cloud computing platform, Clarifai. The machine-predicted tags were analyzed by a network analysis that leads to nine hierarchical clusters. Those clusters were used to distinguish between photos related to CES (65%) and not related to CES (35%). Among the nine clusters, two clusters were related to CES: ‘landscape aesthetics’ and ‘existence’. This step allowed mapping of different aspects of CES and separation of non-relevant photos from further analysis. We further analyzed the impact of protected areas on the spatial pattern of CES and not-related CES photos. The presence of protected areas had a significant positive impact on the areas with both ‘landscape aesthetics’ and ‘existence’ photos: the total number of days in each mapping unit where at least one photo was taken by a user (‘photo-user-day’) increased with the share of protected areas around the location. The presented approach has shown its potential for reliable mapping of socio-cultural uses of landscapes. It is expected to scale well with large numbers of photos and to be easily transferable to different regions.


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