Cross-Modal Learning of Housing Quality in Amsterdam

In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available

Defining and spatially modelling cultural ecosystem services using crowdsourced data

Cultural ecosystem services (CES) are some of the most valuable contributions of ecosystems to human well- being. Nevertheless, these services are often underrepresented in ecosystem service assessments. Defining CES for the purposes of spatial quantification has been challenging because it has been difficult to spatially model CES. However, rapid increases in mobile network connectivity and the use of social media have generated huge amounts of crowdsourced data. This offers an opportunity to define and spatially quantify CES. We inventoried established CES conceptualisations and sources of crowdsourced data to propose a CES definition and typology for spatial quantification. Furthermore, we present the results of three spatial models employing crowdsourced data to measure CES on Texel, a coastal island in the Netherlands. Defining CES as information-flows best enables service quantification. A general typology of eight services is proposed. The spatial models produced distributions consistent with known areas of cultural importance on Texel. However, user representativeness and measurement uncertainties affect our results. Ethical considerations must also be taken into account. Still, crowdsourced data is a valuable source of information to define and model CES due to the level of detail available. This can encourage the representation of CES in ecosystem service assessments

Spatial quantification to examine the effectiveness of payments for ecosystem services: A case study of Costa Rica's Pago de Servicios Ambientales

Payments for ecosystem services (PES) have been developed as a policy instrument to help safeguard the contributions of ecosystems to human well-being. A critical measure of a programme's effectiveness is whether it is generating an additional supply of ecosystem services (ES). So far, there has been limited analysis of PES programmes based on the actual supply of ES. In line with ecosystem accounting principles, we spatially quantified three ES recognised by Costa Rica's Pago de Servicios Ambientales (PSA) programme: carbon storage, soil erosion control and habitat suitability for biodiversity as a cultural ES. We used the machine learning algorithm random forest to model carbon storage, the Revised Universal Soil Loss Equation (RUSLE) to model soil erosion control and Maxent to model habitat suitability. The additional effect of the PSA programme on carbon storage was examined using linear regression. Forested land was found to store 235.3 Mt of carbon, control for 148 Mt yr−1 of soil erosion and contain 762,891 ha of suitable habitat for three iconic but threatened species. PSA areas enrolled in the programme in both 2011 and 2013 were found to store an additional 9 tonC ha−1 on average. As well as enabling a direct quantification of additionality, spatial distribution analysis can help administrators target high-value areas, confirm the conditional supply of ES and support the monetary valuation of ES. Ultimately, this can help improve the social efficiency of payments by enabling a comparison of societal costs and benefits.</p

Understanding the sentiment associated with cultural ecosystem services using images and text from social media

International audienceSocial media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of images as a proxy for CES due to the challenges associated with processing large amounts of this data but this does not fully represent the benefit generated by ecosystems in terms of the positive experiences expressed by users in the associated text. To address this gap, we apply several Computer Vision (CV) and natural language processing (NLP) models to link CES estimates for Great Britain based on the content of images to sentiment measures using the accompanying text, and compare our results to a national, geo-referenced survey of recreational well-being in England. We find that the aesthetic quality of the landscape and the presence of particular wildlife results in more positive sentiment. However, we also find that different physical settings correlate with this sentiment and that sentiment is sometimes more strongly related to social activities than many natural factors. Still, we find significant associations between these CES measures, sentiment and survey data. Our findings illustrate that integrating sentiment analysis with CES measurement can capture some of the positive benefits associated with CES using social media. The additional detail provided by these novel techniques can help to develop more meaningful CES indicators for recreational land use management

Geo-Data for Mapping Scenic Beauty: Exploring the Potential of Remote Sensing and Social Media

Scenic beauty is an important contributing factor to peoples' well-being. Modelling scenic beauty has been made possible at large scales with the availability of open-source remote sensing products. At the same time, the metadata available through social media, including tags and descriptions, offer a novel modelling alternative with a personalised view from the ground. This is especially relevant to policy applications. Using a crowdsourced landscape aesthetics dataset called ScenicOrNot as ground truth, we develop and test models to predict scenic beauty based on remotely sensed indicators and image metadata from social media (Flickr). Initial results show that both model types generate strong predictions of scenic beauty and model accuracy is maximised when the two are combined. Our research shows that both a top-view measurement using remote sensing and a social media-based measurement from the ground can be used to model landscape aesthetics in support of sustainable policy goals

Social media and deep learning capture the aesthetic quality of the landscape

Peoples’ recreation and well-being are closely related to their aesthetic enjoyment of the landscape. Ecosystem service (ES) assessments record the aesthetic contributions of landscapes to peoples’ well-being in support of sustainable policy goals. However, the survey methods available to measure these contributions restrict modelling at large scales. As a result, most studies rely on environmental indicator models but these do not incorporate peoples’ actual use of the landscape. Now, social media has emerged as a rich new source of information to understand human-nature interactions while advances in deep learning have enabled large-scale analysis of the imagery uploaded to these platforms. In this study, we test the accuracy of Flickr and deep learning-based models of landscape quality using a crowdsourced survey in Great Britain. We find that this novel modelling approach generates a strong and comparable level of accuracy versus an indicator model and, in combination, captures additional aesthetic information. At the same time, social media provides a direct measure of individuals’ aesthetic enjoyment, a point of view inaccessible to indicator models, as well as a greater independence of the scale of measurement and insights into how peoples’ appreciation of the landscape changes over time. Our results show how social media and deep learning can support significant advances in modelling the aesthetic contributions of ecosystems for ES assessments

Social media and deep learning reveal specific cultural preferences for biodiversity

Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge. To address this, we trained a deep learning model to capture people's interactions with selected flora and fauna on Flickr as a cultural service related to biodiversity and compared this with citizen science data on iNaturalist, with photos of individual species considered as human–species interactions. After mapping the distribution of people's interactions in Great Britain on Flickr and iNaturalist, we find significant spatial differences in people's preferences on the two platforms. Using a second, pretrained deep learning model, we were also able to identify different preferences for species groups such as birds on social media versus citizen science. To better understand people's preferences, we also compared peoples' interactions with species richness and abundance for a group of 36 bird species, sometimes finding large differences between people's interactions and these ecological measures. Our findings demonstrate that social media can be used to include a wider range of preferences in CES assessments along-side citizen science data. However, these preferences reflect only a limited first-hand experience of biodiversity. Read the free Plain Language Summary for this article on the Journal blog

Social media data for environmental sustainability : A critical review of opportunities, threats, and ethical use

Social media data are transforming sustainability science. However, challenges from restrictions in data accessibility and ethical concerns regarding potential data misuse have threatened this nascent field. Here, we review the literature on the use of social media data in environmental and sustainability research. We find that they can play a novel and irreplaceable role in achieving the UN Sustainable Development Goals by allowing a nuanced understanding of human-nature interactions at scale, observing the dynamics of so-cial-ecological change, and investigating the co-construction of nature values. We reveal threats to data ac-cess and highlight scientific responsibility to address trade-offs between research transparency and privacy protection, while promoting inclusivity. This contributes to a wider societal debate of social media data for sustainability science and for the common good.Peer reviewe

Data for the paper, "Social media data for environmental sustainability: a critical review of opportunities, threats and ethical use"

These data were collected for the paper, "Social media data for environmental sustainability: a critical review of opportunities, threats and ethical use" published in One Earth. It includes a database of studies applying social media data in environmental sustainability research, which were collected and reviewed in full by the authors. Rather than providing a comprehensive summary of all relevant literature like in a systematic review, our objective was to take stock and evaluate the previous body of work in the field in order to promote conceptual innovation from its critical examination. Building on a set of 169 studies collected in a previous systematic review of social media data applications in environmental research (Ghermandi and Sinclair 2019), the database includes additional relevant studies that were identified by snowballing previous references and adding further gray and scientific academic articles known to the authors. For studies to be included in our analysis, they had to involve the use of data from one or more social media platforms and investigate human interactions with and/or impacts on the environment. We relied on a broad definition of social media including any website or application that enables users to create and share content or to participate in social networking (e.g., blogging sites, recommendation sites, and online forums). We further strengthened the analysis by including insights from additional literature on social media that do not have a direct application to environmental sustainability (e.g., studies on biases in social media data). The final database consists of 415 studies, which were published between 2011 and 2021. Ghermandi, Andrea, and Michael Sinclair. "Passive crowdsourcing of social media in environmental research: A systematic map." Global environmental change 55 (2019): 36-47