Terra Analytica

“History as we know it is a series of environmental assessments,” anthropologist Elizabeth Dodd provocatively argues.1 As history-maker, the analysis of environment has come to bear inscriptive properties. The stories told by contemporary analytical tools are written into the earth, and in turn, satellite imagery provides us with a dynamic rendering of these computational processes, materialized as areas of deforestation, low crop yield, contamination (i.e., unusable), or unproductive land, to name a few. The analytical apparatus creates a visualization of finance capital’s random-access memory, constantly updating and rewriting itself.The following traces a genealogy of the automated gaze of corn and soy leading to the conceptualization of our 2018 piece Finis-terra, which began with an interest in the broader cultural significance of the proliferation of analytics, and the methods and imaginaries employed to rationalize those lives not worth computing, not worth measuring, and not worth counting (or accounting for). The artistic research adds to the discussion concerned with the agency of predictive systems and archives to perform the future, as well as the operationality of technological assemblages.2 The historical instances presented constitute an interpretive account of how representational abstraction materially reorganizes land and the species inhabiting it. Through this media genealogy,3 we outline the predictive propensity of views from above through various examples, considering how knowledge is organized and produced through these lenses.The genealogy thus begins with an anecdote toward the politicized nature of labor mechanization that serves to contextualize the discourse of efficiency that shrouds the history of technological development, and to discuss how the aesthetics produced by industrial agriculture become machine readable and compatible with the cybernetic conception of systems. Simultaneous developments in early photogrammetry apparatuses served to interpret crop analysis, informing the legacy of early pattern recognition and interpretation in today’s geospatial intelligence systems. From this curation of critical historiography, the text moves toward a consideration of the military actors driving the technological development of software such as René Descartes, sketching the stakes of space as a territory of preemption, and its concomitant ontological implications. The later part of the essay brings together figures that informed the making of Finis-terra: the Spring Carpet that introduces the expression of ordering nature as individuated elements from which greater generalizations can be inferred, and the hammock, which bridges colonial capture of space and peoples made possible through the Cartesian separation between observer and observed inherent in environmental analysis.Finis-terra itself is a set of hammocks, each comprised of silk and wool woven patterns depicting crop yield prediction through the Descartes software, which couples satellite imagery and machine learning–based pattern recognition. The square shapes discernable in Finis-terra’s weave designate fields identified by crop segmentation, and the color denotes the forecasted crop yield, indicating “real time” value. These prognostications inform live commodity trading and insurance valuation.4 The assumed certainty upon which these exchanges are predicated exemplify the reorientation from data interpretation to data intelligence in the realm of geospatial information systems. The installation, which is mainly experienced by lying in it rather than looking at it, attempts to break with the subjectifying gaze of remote-sensing apparatuses. Following this, we conclude by threading possible decolonial epistemologies that explore alternative modes of understanding the world through the body, guided by the Andean philosophies of Silvia Rivera Cusicanqui, which call for an embodied-earth-thinking.One could posit that the automated gaze that environmental analytics afford is based in how machine vision is entangled with the abstraction of labor. Lewis Mumford famously wrote that new colonists lacked labor power and were therefore propelled to invent labor-abstracting agricultural devices.5 Mumford overlooked the messiness of human laborers and their ability to organize in demand of basic requirements as a motivational factor to develop obedient mechanical replacements: cyber arms.The politicized nature of labor mechanization is exemplified through the foundational marriage of agricultural engineering and plant breeding, which occurred between the cultivar VF-145 (a so-called square tomato) and the UC Blackwelder mechanical tomato harvester. Both are engineering marvels, a culinary tragedy and a testimony to the overlooked anti-unionist and xenophobic incentive to develop machinery and remote-controlled equipment for harvest.In the mid-1950s plant breeder Gordie Hanna and agricultural engineer Coby Lorenzen, two scientists at the University of California, Davis, teamed up to invent a machine that could mechanically harvest tomatoes. Devices were optimized for monoculture production; however, more crucially, a tomato species was bred to withstand the rough touch and bulk handling of machine harvesting. It is often reported that Hanna literally threw tomatoes on the highway from a nearby bridge to test their resistance to impact.6By 1962, the machine and the plant were market ready. Two years later, the United States Government refused to extend the provision of Public Law 78 allowing foreign nationals, who are often referred to as “otherwise inadmissible aliens,”7 into the US to help with crop production and harvesting.8 This is cleverly alluded to in Alex Rivera’s mock-marketing film Why Cybraceros? (2012) in which the ultimate American Dream is one where immigrants can labor in the US remotely, never having to live in, or become the responsibility of, American society.9 A short animated demonstration within the video depicts stereotyped Mexican workers controlling robotic harvesters in the US from across the border in Mexico. Rivera’s video sarcastically posits these caricatures as trouble-free, low-cost laborers for which the state is not accountable: the perfect immigrant.The mechanization of labor ushered by monoculture engineering developments, such as the UC Blackwelder mechanical tomato harvester, also brought about carefully delineated fields that unintentionally catered to the eye in the sky. Fields serving mechanical harvesters were characteristically geometrical, for example in the shape of long rectangles to accommodate the machine’s preference for straight lines, and a width a minimum of twice that of the machine, allowing for the necessary 180-degree turn at the end of each lap. Checkered lines such as those pictured in the dry belt near Champion, Alberta, would inspire a mathematical reading of space through calculable parameters. Such neatly isolated variables could then inform narratives of economic growth and facilitate the application of the circular causality and regulatory logics of cybernetics to industrial agriculture. The prairie township fields reminiscent of Hanna’s square tomato became surfaces, which are easier to capture, measure, and valuate.The “input” and “output” paradigms that dominate computational and financial operations could now easily be measured in land allocations such as the following:INPUTPetroleumFuel OilRaw SugarMotor VehiclesOUTPUTGrainNewsprintFlourLumberThus, the tools mechanical harvesting produced ushered in an aesthetics of machine-readability that also facilitated narratives of circular causality.These aesthetics also facilitated crop analysis using photogrammetry based on views from above. Contemporaneous modes of capture, measure, and valuation, along with forecasting based on machine learning, are rooted in a tradition of methods earlier developed for interpreting aerial photography. Before these photographs were bulk processed like Californian tomatoes by trained algorithms, pattern recognition and analysis were termed “photographic interpretation.”10 In 1960, the preface of the American Society of Photogrammetry and Remote Sensing’s volume Photographic Interpretation described the practice as akin to “detectives who are involved in the extraction of valuable information from aerial photographs…obscured by nature’s camouflage.”11 Massive fields dedicated to monoculture were one of the areas where such extraction was pioneered.Photogrammetry was largely used to survey geologic structures, drainage patterns, and agricultural areas, which could indicate lithology and soil structure as well as crop yield forecasting. Various types of film and filters were employed to capture oblique and vertical views, such as Camouflage Detection and Ektachrome film, or Panchromatic 25 A and Infrared 89 A filters. Depending on the film or filter, certain wavelengths are largely absorbed while others are reflected, and these properties vary by crop type, or for example when a plant is diseased. In a diseased leaf, the spongy mesophyll tissue, which is usually highly reflective of infrared light, collapses while for a time retaining its green color. Thus, the infrared film can detect crop disease before it is observable to panchromatic photography.12 As such, as a means of cross-referencing with a more complete spectrum, several images of an area were aggregated and compared for analysis, in combination with the use of vertical stereograms to understand topography. Stereograms were created by using two images of an area taken from the same height at different angles and collated with the help of a mirror or scanning stereoscope with a binocular attachment used to plot boundaries or terrain features on the aerial photographs. Photographs were aligned precisely with match lines and annotated with colored pencils according to land use.13 The analysis of these images was determined with pattern detection, usually a combination of geologic, drainage, or crop pattern features.Crop patterns in aerial photography were largely determined through tone and texture. The tone had an array of values from nearly white to light gray, and the texture spanned such values as reticular, cordlike, striate, and linear. From these texture and pattern values, we can infer the future algorithms’ pseudocode.14 As an example, based on land use categories employed in Switzerland circa 1960,15 we can imagine the following instruction set:If tone is nearly white and texture is speckled, type of land use is Field.If tone is very dark and texture is cloudy, type of land use is Improved meadows.If tone is light and texture is partly dotted, type of land use is Pasture.If land use is field and tone is light gray, field is Small Grain.If land use is field and tone is nearly white, field is Potatoes.If land use is field and tone is light, field is Barley or Wheat.Else land use is Unimproved Meadows.In the 1960s, while automation was being researched, the analysis central to photogrammetry was being accomplished by an interpreter, invariably a human, and usually female-identified.16 It was also understood that the properties that the interpreter was trained to detect—texture, pattern, tone, or shape—were qualitative and therefore appraised subjectively.17 The complexity of this appraisal also lay in the necessary knowledge of the multifarious interrelations between soil, plants, and weathering processes, together with experience in recognizing their combined agencies. A number of questions were provided to interpreters during training to aid the assessment process, such as: “Is the pattern location related to: (a) land, (b) water, (c) continent, (d) subcontinent, (e) regional, (f) local?”; “Is the pattern[:] a) random or (b) systematic?”; “Is the tone of the pattern[:] (a) Consistent, (b) Lighter, or (c) Darker?”; “Is the pattern a unit in itself or subject to breakdown of identity?”; or “Is the pattern near or related to similar patterns?”18 However, the conclusion was ultimately understood to be a subjective process achieved through the interpreter’s deduction. As the task of interpreting has been largely delegated to computational assemblages, such as Descartes Lab, it has come to be considered an objective form of intelligence. Thus, the methods and conclusions inferred from the female-identified human, which were deemed subjective interpretation, became understood as objective when performed by the machinic.Photo interpretation was also key in agricultural economics.19 In the US, yield was forecasted for commercially important crops by the Department of Agriculture. Thus, wartime photography analysis became integrated into Rural Sociology, a then-new field dedicated to the economics of farm production.20 Perhaps unsurprisingly, software based on pattern recognition, which is used to predict crop yield contemporaneously, is an area of significant interest to military organizations, namely the US Defense Advanced Research Projects Agency (DARPA).21 In 2017, DARPA solicited for its to develop for and geospatial The research agency has been making significant into machine pattern software that satellite imagery to the of from low crop by agricultural commodity or is now as a key for such as is referred to by as the to these in and a in a in and an of in to production for The leading to and the in the this the in of the is that has been to that from the of This introduces a new to it to it is with military of the that has a significant of through is the Descartes Lab, a software development on the provision of geospatial intelligence through the automation of analysis of satellite imagery and It is René Descartes, to we the Cartesian which the of an through also the separation of and in a that us Descartes was in as a from the of the the and a for In to so-called Descartes a that is through a A of imagery is to with and understand how the to the and data a account more that one be was created with the this of an array of images upon which certain could be such as through of infrared light from the together with The of this data and these is through the the and understanding of the as well as the together with the very satellite imagery made to the creates its is to this and at is in considering the power at in geospatial intelligence machine has of that machine patterns in data be in the of with gaze of the has trained us to understand time as vision through the of In to from is to from the From this it that to from above is to from the As prediction based on geospatial intelligence has insurance valuation, commodity market and military not making and also in the eye in the an the future through its made to the from which had a on the the computational of machine with data provides the contemporary ordering of the has referred to as a new The by various elements in the production of and However, machine not understand pattern recognition, or data analysis, is rather based in the of while this to earlier in which the between two images is used to infer machine is more akin to through these and dynamic modes of knowledge production and of the future itself from the of new by The of The of in and Society to the in by As to the or that as by their and itself by looking of capture such as geospatial intelligence of abstraction that facilitate the by which the of the making form of into prediction from the of the making the with or yield a that can be of the thus the of prediction is predicated upon a future looking toward the from one to the from the future, to in its silk and a space to the same the woven and patterns to the of this by the and the of how is central to the training of to patterns in and knowledge in this based on a set of readable from the and of a satellite and rooted in the predictive of the from above. we then understand the of crops being with the are being the for the being to earlier of pattern recognition software is the new valuable information from aerial photographs. such as Descartes Lab, which described itself in as predictive an ontological from interpretation by female-identified to intelligence. This the of crops with where the is a field that agricultural and crop as well as so-called conception in which the field has the ability to and a of elements while on methods developed in satellite imagery is with trained to several of patterns, an process of recognition. 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