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- Convenors:
-
Lydia Gibson
(Columbia University)
Tone Walford (University College London)
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- Format:
- Panel
- Sessions:
- Monday 6 June, -
Time zone: Europe/London
Short Abstract:
Grounding remote sensing and environmental AI in their sprawling infrastructures of data practices, actors, political and economic realities, and extractive legacies to better scrutinize their role in existing fieldwork structures and hierarchies, and the violence of data promises and erasures.
Long Abstract:
Remote sensing is fast becoming a mainstay of ecological methodology, replacing in-situ field observations. Many environmental scientists welcome this, citing high cost and carbon emissions of air travel, complex geopolitics, and fractious local sociopolitical structures as reasons to ostensibly decarbonise, depoliticise and “desituate” environmental research. Social science has taken notice of this shift, describing the automated sensors and monitors that now litter landscapes, and the drones and satellites that surveil them overhead, as a form of warfare; where dystopian Orwellian slow violence is intersected by rapid, necropolitical insurgences of military complexes. What is often overlooked, however, is that these instruments, sensors and technologies are just the most visible elements of sprawling informational infrastructures, protruding from and constituting capillary networks of data, technical arrangements, actors, actants, legacies, hierarchies, and imaginaries of environmental futures.
In this proposed panel we seek to provoke a broader scrutiny of automation, remoteness, and the desituation and depoliticization of environmental data, by grounding remote sensing and environmental AI in the geopolitical, topographic, and economic realities that surround and produce it. We invite contributions that explore spaces of power opened out by remote sensing and environmental AI; scrutinise their interdigitation with local environmental imaginaries and policies; reveal how the inequalities and injustices that have gone unresolved within field research are baked into databases, models, and algorithms; and render visible their reiteration of colonial and extractive relationships. In what ways do the ambivalent promises and erasures of interscalar data interact to (re)produce the contemporary violences within environmental research?
Accepted papers:
Session 1 Monday 6 June, 2022, -Paper short abstract:
This paper will present the imaginaries that animate trans-national environmental data infrastructures, and how these might be sustained or challenged by how environmental data emerges “on the ground”, out of interwoven and localised socio-material, political, historical contexts and configurations.
Paper long abstract:
In this paper I’ll be thinking through the sorts of planetary imaginaries that animate large-scale, trans-national environmental data infrastructures, and how these might be sustained or challenged by how environmental data is produced “on the ground”, out of interwoven localised relational, material, political and historical contexts and configurations. The planetary aspirations of international environmental data infrastructures seems to point to an idea of a patchwork planet, which carries with it new ideas and hopes for informational democracy, liberalism and justice, and in which we see complex environmental problems becoming data problems that require ‘joined-up’ or sutured data solutions. Drawing on ethnographic work with data technicians and scientific researchers in the Brazilian Amazon, in this paper I will draw out the ambivalences and contradictions of this imaginary as it comes up against people’s everyday experiences of working with environmental data in the Brazilian Amazon. It is crucial here to take into account the enduring historical present of imperial and colonial formations of environmental science in the Amazon - either as these formations exert pressure to shape people's experiences, or as they seem to disappear from view altogether - in any attempt to think through what environmental data justice might be.
Paper short abstract:
This paper describes uses of satellite data among Sámi reindeer herders in northern Sweden. I discuss these uses in relation to Sweden’s ongoing efforts to develop small satellite launch capability and historical constructions of the circumpolar North as a welfare frontier.
Paper long abstract:
In January 2022, Swedish newspapers reported on the discovery of rapidly growing cracks in buildings around the city of Kiruna. Using satellite data, the National Space Agency could identify the cause: ground movements at the local iron ore mine. This application of satellite data forms part of a long-running campaign by Swedish space advocates to demonstrate the public benefit of space infrastructure and remote sensing, especially in the context of environmental research. Over the past decades, a similar impulse has promoted satellite data to manage reindeer herding. This initiative is currently administered by the Sámi Parliament and used by herders to manage their lands and track reindeer-landscape relations. However, the relation between the Swedish space sector and the Sámi is far from frictionless. In 2021, the Sámi Council halted a geoengineering experiment that was to be conducted at the Swedish Space Corporation’s rocket launch-site outside Kiruna. Moreover, the ongoing expansion of the Swedish space centre is raising concerns about potential impacts on the reindeer and access to grazing. In this paper, I discuss my fieldwork on Sweden’s efforts to develop small satellite launch capability, linking these activities to historical constructions of the circumpolar North as a welfare frontier. In doing so, I elicit the troubling and complex legacies of Swedish remote sensing infrastructures. Examining these infrastructures reveals an awkward relationship as satellite data is instrumentalised to support herders and monitor other extractive industries long contested by the Sámi.
Paper short abstract:
In this paper I aim to show how machine learning applied within the domain of remote sensing serves to strengthen the hegemony of the techno-scientific world-making project. I further propose that it is necessary to reverse-engineer the God-trick and to ground the view from nowhere.
Paper long abstract:
In this paper I argue that environmental science is implicated in a particular world-making project whose basic logic is to flatten the multiple co-existing worldspaces of the pluriverse and to produce a world-image that brackets embodied ways of enacting the world in favor of the world as viewed from the view from nowhere. I further highlight this dynamic as it appears in the application of machine learning within the domain of remote sensing. Remote sensing is fast becoming one of the domains of environmental science in which machine learning is becoming a methodological mainstay. In order to contest the denigration of the more-than-human world and the ontological hegemony of the techno-scientific world-making project, I propose that it is necessary to reverse-engineer the God-trick. Thus, in this paper I aim to bring into view the intricate capillary networks of digital knowledge infrastructures from which the combination of remote sensing and machine learning emerges, and to reframe the view from nowhere as a phenomena that is specific to particular practices of handling data. I focus specifically on the technique of feature extraction and show how one of the novel effects brought forth with machine learning techniques is the ability to combine features within digital hyperdimensional planes. The ability to relate features with an n-dimensional hyperplane sets the stage for an almost endless succession of combining and recombining features, a phenomena for which I propose the term hyper-combinatorialism. This may be viewed as a considerable amplification of the decontextualizing capacity of environmental science.
Paper short abstract:
GEDI – a satellite LIDAR designed for measuring forest structure – is said to revolutionise remote sensing and monitoring. This paper considers the political economy of GEDI data products across scientific units and its applicability to small, postcolonial island nations with inaccessible forests
Paper long abstract:
In 2018, GEDI – The Global Ecosystem Dynamics Investigation – was deployed on the International Space Station for a two-year mission (since renewed for a further five). GEDI is the first satellite LIDAR designed specifically for measuring the canopy height, vertical structure, and surface elevation of tropical and temperate forests, with a cluster of high-powered, full-bean lasers designed to penetrate the densest of canopy cover and return reliable reflected waveform data from which vegetative structure can be deduced. Its applications include largescale, long-term monitoring of deforestation and forest composition and structure. The training data used to create waveform algorithms is almost exclusively from North America and Europe, where relative ease of access has supported a wealth of fieldwork and associated ground data. GEDI’s use meanwhile is largely targeted at dense tropical forests in the global south where remote sensing can compensate for the geographical and political inaccessibility that precludes many data collection efforts.
The aim of this paper is twofold. First it explores the political economy of the launch of GEDI, in which Python programmers are courted and seduced by novel libraries and visualisations and through a series of webinars and tutorials of its use in familiar IDEs and environments, thus securing end users for the GEDI data products. Second, it takes as an example small island developing states (SIDS) to show how inaccuracies are exacerbated and relative spatial coverage reduced in the hundreds of SIDS protected forests to which GEDI data products might likely be applied.