According to Google: How does Google Images see and show climate change and biodiversity loss? Data collection Data curation Visualisation and Analysis The Google Images search engine is probably the most important online gatekeeper of visual culture worldwide, with over two billion searches conducted on the platform every day. Despite the platform’s importance, there is a scarcity of analyses on how Google determines the visibility of images within its search rankings. For example, is “authority” determined in a similar way to its textual web search results? Or do computer vision techniques offer new criteria for promoting images and the websites that host them? This research project focused on the visualisation of [climate change] and [biodiversity loss] by building a dataset of country-specific search rankings from Google Images. We scraped data from six different countries, selected for their political and geographic diversity in relation to the environment: Australia, Netherlands, Nigeria, China, Mexico and Brazil. We built on findings from a 2019 DMI Summer School project, Climate Image Spaces, that suggested Google Images shows a dehumanised visual vernacular of climate change dominated by landscapes, charts and the Earth. Google Images has a homogenising effect on the visual vernaculars of both climate change and biodiversity loss. For climate change, generic representations of climate change dominate. In particular, a small number of “iconic” stock images are highly ranked across the different countries. Many of these images have a half and half “left/right” layout suggesting the current, often dystopian, situation on the left, with a more hopeful utopian future on the right (occasionally this implied temporality is reversed). Examples of these images include “hand in earth”, “landscape” and “tree”. For biodiversity loss, there is also homogeneity, although with some greater country-specific diversity. Stock imagery is less dominant, but half and half images remain important, such as ‘forest as lungs’. Although biodiversity loss covers many different aspects of environmental degradation, the imagery focuses almost exclusively on deforestation. Images appear to be ranked on whether they “accord” with Google’s envisioning of climate change as time-less, place- less, human-less and cause-less. Websites not according to Google’s visions of climate change and biodiversity loss are unlikely to be ranked highly by Google Images. We used a Python script and VPNs to collect the top 50 images associated with [biodiversity loss] and [climate change] on Google Images for six countries: China, Brazil, Netherlands, Nigeria, Mexico. This resulted in a corpus of 300 images for each search term. We identified the most similar/mobile images across datasets using Google Cloud Vision, Clarifai and Memespector. Using vision APIs we identified labels and web entities associated with the images, which we used to gain a deeper understanding of the logic underpinning the sorting of images. Manual coding was then used to identify specific types of images which were not characterised by Google or Clarifai labels (e.g. half and half images, degraded forests). Using semiotics to inform our analysis manual coding served to differentiate between identical and similar images. Country specific datasets were then merged into a master spreadsheet for each term and used to identify top 10 images. We repeated the same process and expanded our data collection using the Search Engine Scraper developed by the DMI to scrape the top 10 ranking websites of Google. We extracted web domains and used the Triangulate tool to compare Google's Search and Images results in each country. Data and protocols were collated in a shared Google Drive folder. A range of tools were used for data collection, in particular a Python script and DownThemAll, and for data analysis: Clarifai, Cloud Vision, Memespector, Imagesorter, Rawgraph and Gephi. Data was centralised in csv for