Tag Archives: real-time maps

Air Quality

The tracking of local air quality is a contemporaneous way to track the effects of the fire seige that initiated clusters of fires across the western seaboard to be ignited at the end of a long, dry summer from August 25. We were not really struck unawares by the dry lightning, but had left forests languishing, not beneath electricity lines–as last year, around this time–but under a hot sun, and high temperatures that we hardly registered as changing the ecosystem and forest floor. This year, the sun turning red like a traffic light in the middle of the afternoon, we were forced to assess the air quality as the blue sky was filled with black carbon plumes from nearby fires that at times left a grittiness in our eyes.

Scott Soriano, September 27 2020
October 1, 2020

Confronted with a red sun through pyrocumulus haze, we followed real-time surveys of air quality with renewed attentiveness as an orange pyrocumulus clouds blanketed usually blue skies of the Bay Area, obscuring the sun’s light, suffusing the atmosphere with a weirdly apocalyptic muted light, that were hardly only incidental casualties of the raging fires that destroyed houses, property, and natural habitat–for they revealed the lack of sustainability of our warming global environment.

EPA/World Air Quality Index/New York Times September 15, 2020

The soot and fog that permeated “clean cities” like Portland and San Francisco came as a sudden spike in relation to the black carbon loads that rose in plumes from the fires, as if the payload of the first bombs set by climate change. The shifting demand for information that evolved as we sought better bearings in the new maps of fires that had become a clearly undeniably part of our landscape was reflected in the skill with which the sites of incidence of dry lighting strikes that hit dried out brush and forest floors, the growing perimiters of fires and evacuation zones across the west coast, and the plumes of atmospheric smoke of black carbon that would leave a permanent trace upon the land, liked to the after-effects of holocausts created by atom bombs by Mike Davis. The measurement of wind carrying airborne smoke emerged as a layer of meaning we were beginning to grasp, a ghostly after-effects of the fields of flams that began from sites of lightning hitting the earth in a Mapbox wildfire map of fields of fire across the states, radiating resonant waves akin to earthquake aftershocks, a lamination on hex bins of the fires that seemed a new aspect indicating their presence in the anthropocene.

The suitably charcoal grey base-map of the state integrates approximate origins of fires, fire spread and greatest intensity of hotspots from satellite imagery courtesy Descartes Labs and NOAA, and air pollution data integrates the fires’ spread across our picture of the state. While human reviewed and sourced, the satellite data embodies the ravages of fire across the state in ways echoed by its black charcoal base map, and reflects the need to develop new visual tools to process their devastation.

Mapbox Wildfire Maps/CalFire Data/OpenStreetMap/Los Angeles Times Sept 28, 2020

While we began to measure air quality to meet new needs to track ground-level ozone, acid rain, air toxins, and ozone depletion at an atmospheric level, the increased tracking of more common air pollutants since 1990 included airborne particulate matter (PM10 and PM2.5), carbon monoxide (CO), and ozone (O3), we track the effects of wildfire smoke by hourly levels of each at local points, parlaying sensors into newsfeeds as wildfires rage. If stocked with labels of each chromatic layer, are these real-time updates lacking not only legends–but the temporal graph that would clarify the shifting data feeds that lead us to give them the illusion of purchase on the lay of the land we are trying to acknowledge this fire season?

Berekeley, CA October 1, 2020/Clara Brownstein

Watching slightly more long-term shifts in quality of air that we breath in the Bay Area, we can see striking spikes of a maximum just after the lighting siege began on August 19, 2020 across much of the state, as air quality decisively entered into a hazardous zone, tracking PPM2.5 concentrations, but entering the worst fifteen air days since registration four times since 1999, when Bay Area Air Quality Management District began reporting the levels of fire smoke in inhabited areas.

Particulate Matter (PM 2.5) Concentrations in Bay Area, August 15-Septmeber 13, 2020/
Bay Area Air Quality Management District

We measure fires by acreage, but the sudden spikes of air quality, while not exceeding the smoke that funneled into the Bay Area during the North Bay Fires in 2017, when the Tubbs and Atlas Fires devastated much of the Wine Country, created a run of high-smoke days, were followed by a set of sudden spikes of the atmospheric presence of particulate matter that we tried to track by isochomes, based on real-time sensor reading, but that emerge in better clarity only in retrospect.

It is true that while the AQI maps that offer snapshots of crisp clarity of unhealthy air might serve as an alarm to close windows, remain indoors, and call off school–

AirNow AQI map in Bay Area after Lightning Fires, August 22, 2020

–as particulate matter spread across the region’s atmosphere. We are used to weather maps and microclimates in the Bay Area, but the real-time map of particulate matter, we immediately feared, did not only describe a condition that would quickly change but marked the start of a fire season.

Not only in recent days did the sustained levels of bad air suggest an apocalyptic layer that blanketed out the sun and sky, that made one feel like one was indeed living on another planet where the sun was masked–a sense heightened by the red suns, piercing through grey smoke-cover that had seamlessly combined with fog. Although the new landscapes of these AQI maps generate immediate existential panic, we should be more panicked that while we call these fires wild, they release unprecedented levels of toxins once imagined to be detected as industrial pollutants. The seemingly sudden ways that black carbon soot blanketed the Bay Area, resting on our car hoods, porches, windowsills and garbage bins were not only an instant record of climate emergency, but the recoil of overly dry woods, parched forests and lands as overdue payback for a far drier than normal winter, months and a contracted rainy season that had long ago pushed the entire state into record territory. The lack of soil moisture has brought a huge increase of wildfire risk, not easily following the maps of previous fire history, and persistence of “abnormally dry” conditions across a third of California, focussed in the Sierra and Central Valley–the areas whose forests’ fuel loads arrive carbonized in particulate form.

Local monitors of air quality suggest the uneven nature of these actual isochromes as maps–they are reconstructions of what can only be sensed locally, and does not exist in any tangible way we can perceive–but presented what we needed to see in a tiler that made differences popped, highlighting what mattered, in ways that left cities fall into the bottom of the new colors that blanketed the state, in which local sensors somehow revealed what really mattered on August 20: if the “map” is only a snapshot of one moment, it showed the state awash in ozone and PPM.

AirNow/August 20, 2020
Air Quality Index

We were in a sort of existential unfolding in relation to these maps, even if we could also read them as reminders of what might be called “deep history”: deep history was introduced by Annalistes to trace climatic shifts, the deep “undersea” shifts of time, on which events lie as flotsam, moved by their deep currents that ripple across the economy in agrarian societies, suggesting changes from which modern society is in some sense free. “Deep History” has to some extent been reborn via neurosciences, as a history of the evolution of the mind, and of cognition, in a sort of master-narrative of the changes of human cognition and perception that makes much else seem epiphenomenal. If the below real-time map was time-stamped, it suggested a deep history of climate of a more specific variety: it was a map of one moment, but was perched atop a year of parched forests, lack of groundwater, and increased surface temperatures across the west: Sacramento had not received rain since February in an extremely dry winter; its inter was 46% drier than normal, and the winder in Fresno was 45% dryer in February. They are, in other words, both real-time and deep maps, and demand that we toggle between these maps as the true “layers” of ecological map on which we might gain purchase.

The levels of dessication of course didn’t follow clear boundaries we trace on maps. But at some existential level, these flows of particulate matter were not only snapshots but presented the culmination and confirmation of deep trends. We have to grasp these trends, to position ourselves in an adequate relation to their content. For the deep picture was grim: most of California had enjoyed barely half of usual precipitation levels after a very dry winter: Sacramento has had barely half of usual rainfall as of August 20 (51%); the Bay Area. 51%; parts of the Sierra, just 24%. And wen we measure smoke, we see the consequences of persistent aridity.

August 28, 2020/AirNow
Air Quality Index

These are the layers, however, that the maps should make visible, And while these shifts of particulate matter that arrived in the Bay Area were invisible to most, they were not imperceivable; however, the waves of smoke that arrived with a local visibility that almost blanketed out the sun. Perhaps there was greater tolerance earlier, tantamount to an ecclipse. Perhaps that seemed almost a breaking point.

For almost a month after the first fires broke, following a sequence of bad air days and spare-the-air alerts marked our collective entrance to a new era of climate and fire seasons, fine soot blanketed the state at hazardous levels, leaving the sense there was nowhere left to go to escape.

September 13, 2020
Air Quality Index

We had of course entered the “Very Unhealthy” zone. If real-time maps condense an immense amount of information, the snapshot like fashion in which they synthesized local readings are somewhat hard to process, unless one reads them with something like a circumscribed objective historical perspective that the levels of PPM5 provides. In maps that are data maps, and not land maps, we need a new legend, as it were, an explanation of the data that is being tracked, lest it be overwhelmed in colors, and muddy the issues, and also a table that will put information on the table, lest the map layers be reduced to eye candy of shock value, and we are left to struggle with the inability to process the new scale of fires, so unprecedented and so different from the past, as we try to gain bearings on our relation to them.

Of course, the real-time manner that we consume the “news” today

militates against that, with feeds dominating over context, and fire maps resembling increasingly weather maps, as if to suggest we all have the skills to read them and they present the most pressing reality of the moment. But while weather maps suggest a record of the present, these are not only of the current moment that they register. Looking at them with regularity, one feels the loss of a lack of incorporating the data trends they depict, and that are really the basis of the point-based maps that we are processed for us to meet the demand for information at the moment, we are stunned at the images’ commanding power of attention to make us look at their fluid bounds, but leave us at sea in regards to our relation to what is traced by the contour lines of those isochrones.

Bay Area Air Quality Management, PM2.5 Concentrations, August 15-September 13, 2020

We can, in the Bay Area, finally breathe. But the larger point re: data visualizations is, perhaps, a symptom of our inflow of newsfeeds, and lies in those very tracking maps–and apps–that focus on foregrounding trends, and does so to the exclusion of deeper trends that underly them, and that–despite all our knowledge otherwise–threatens to take our eyes off of them. When the FOX newscaster Tucker Carlson cunningly elided the spread of wild fires ties to macro-process of climate change, calling them “liberal talking points,” separate from climate change, resonating with recent calls for social justice movements to end systematic racism in the country: although “you can’t see it, but rest assured, its everywhere, it’s deadly. . . . and it’s your fault,” in which climate change morphed to but a “partisan talking point” as akin to “systematic racism in the sky.”

While the deep nature of the underlying mechanics by which climate change has prepared for a drier and more combustable terrain in California is hard to map onto to the spread of fires on satellite maps, When climate denialism is twinned with calls for reparations of social injustice or gun control as self-serving narratives to pursue agendas of greater governmental controls to circumscribe liberties, befitting a rant of nationalist rage: the explanations on “our” lifestyles and increased carbon emissions, only pretenses to restrict choices we are entitled to make, Carlson was right about the depths at which both climate change and systematic racism offer liberal “lies”–especially if we squint at tracking maps at a remove from deep histories, and cast them as concealing sinister political interests and agendas, the truly dark forces of the sinister aims of governmental over-reach in local affairs.

“Structural racism” is indeed akin to the deep structure of climate change if the cunning analogy Tucker Carlson powerfully crafted for viewers did not capture the extent of their similarities. For if both manifest deep casualties created by our society, both depart from normalcy and both stand to hurt the very whites who see them as most offensive. The extent of inequalities of systematic racism as present in our day-to-day life as is the drying out landscape. And the scope of climate change is able to be most clearly registered by the evident in trends of diminished precipitation, groundwater reserves or temperature change that create environmental inequalities, too often obscured by the events of local air quality or maps of social protests that respond to deep lying trends.

To be sure, the tracking of environmental pollutants underlay the national Pollution Prevention Act of 1990, and led to a number of executive orders that were aimed to set standards for environmental justice among minority communities who long bore the brunt of industrial pollutants, from lead paint to polluted waters to hazardous waste incinerators. And, as we are surrounded by racial inequalities that are visible in systematic inequalities before the law, and have lowered life expectancies of non-whites in America by 3.5 years, increasing rates of hypertension, cancer, and systematic disenfranchisement of blacks–these extensive inequalities hurt whites, and hurt society. As Ibrahim X. Kendi perceptively noted, White Supremacists affirm the very policies that benefit racist policies even when they undercut interests of White people; they “claim to be pro-White but refuse to acknowledge that climate change is having a disastrous impact on the earth White people inhabit.” Is there a degree of self-hatred that among Carlson’s viewers that informs Carlson’s frontal attack on climate change and structural racism as myths, more content to blame non-Whites for structural inequalities.

But these inequalities are evident in the differences in air quality that climate change creates. For if the AQI maps tell us anything, it is the absence of any preparedness for the interconnections of fire, smoke, and large dry stretches of a long story of low precipitation that have created abnormally dry conditions–indeed, drought–across the state.

California Drought Monitor, Sept. 17, 2020/Brad Rippey, U.S. Department of Agriculture

The intensity of severe drought across the conifer-dense range Sierras raises pressing questions of federal management of lands: the moderate to severe drought of forested lands intersect with the USDA Forest Service manage and the over 15 million acres of public lands managed by the federal government manages or serves as a steward.

–that crosses many of the dried out wildland and rangeland forested with conifers and dense brush, a majority of which are managed by federal agencies–19 million acres, or 57%– but with climate change are increasingly drier and drier, which only 9 million are privately owned.

Ownerships of California Forests and Rangeland
USDA Forest Service Management (Purple), National Parks (Lavender), Bureau of Land Management (Orange)

Yet the reduction of Wildland Fire management by 43.98% from FY2020 to FY2021 in President Trump’s budget continued the systematic erosion of funding for the United States Forest Services. As California weathered longer and longer fire seasons under Donald Trump’s watch, Trump made budget cuts $948 million to the Forest Service for fiscal year 2020, after defunding of US Forest Services by reducing mitigating fire risk by $300 million from FY 2017 to FY2019, cutting $20.7 minion from wildlife habitat management, and $18 million from vegetation management–a rampage beginning with cutting USFS research funding by 10% and Wildland Fire Management by 12% in FY 2018! While blaming states for not clearing brush in forests, sustained hampering of managing federal lands rendered the West far less prepared for climate change. As the costs of containing wildfires rise, the reduction of the Forest Service budget has provoked panic by zeroing out funding for Land and Water conservation–alleged goals of the Trump Presidency–and cuts grants to state wildfire plans by a sixth as fire suppression looms ever larger.

By defunding of forest management, rangeland research, and habitat management, such budgetary measures pose pressing questions of our preparedness for the growing fire seasons of future years; stars that denote public land management might be targets for future dry lightning.

Ecosystems of California (2016)

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Filed under Climate Change, climate monitoring, climate sciences, data visualization, fires

Earth, Wind, and Fire

We think of earth, wind and fire as elements. Or we used to. For the possibility of separating them is called into question in the Bay Area, as wind sweeps the smoke of five to seven fires, or fire complexes, across the skies, we are increasingly likely to see them as layers, which interact in a puzzle we have trouble figuring out. Indeed, the weirdly haunting
map of air quality to map the atmospheric presence of particulate matter by isochrones brought late summer blues to the Bay Area. Blue skies of the Bay Area were colored grey, burnt orange, and grey again as cartoon plumes of soot flooded the skies in a new sort of pyrocumulus clouds that turned the sun red, offering a disembodied traffic sign telling us to stop.

Clara Brownstein/October 1, 2020

Fire season began by remapping the town in terrifying red that registered “unhealthful,” but almost verging on the “hazardous” level of brown, based on local sensors monitoring of ozone, but is also registering a deeper history defined by an absence of rain, the lack of groundwater, the hotter temperatures of the region and the dry air. The map is both existential, and ephemeral, but also the substrate of deep climate trends.

AQI Chart on Saturday, August 22, 2020/AirNow (EPA)

Is fire an element we had never before tracked so attentively in maps? We did not think it could travel, or had feet. But wildfire smoke had blanketed the region, in ways that were not nearly as visible as it would be, but that the real-time map registers at the sort of pace we have become accustomed in real-time fire maps that we consult with regularity to track the containment and perimeters of fires that are now spreading faster and faster than they ever have in previous years. And soon after we worried increasingly about risks of airborne transmission of COVID-19, this fire season the intensity of particulate pollutants in the atmosphere contributed intense panic to the tangibility of mapping the pyrocumulus plumes that made their way over the Bay Area in late August. As the danger of droplets four micrometers in diameter remaining airborne seemed a factor of large-scale clusters, the waves of black carbon mapped in the Bay Area became a second sort of airborne pathogen made acutely material in layers of real-time Air Quality charts.

The boundaries of fire risk charts and indeed fire perimeters seemed suddenly far more fluid than we had been accustomed. When we make our fire maps with clear edges, however, it is striking that almost we stop registering the built environment, or inhabited world. As if by the magic of cartographical selectivity, we bracket the city–the sprawling agglomeration of the Bay Area–from the maps tracking the destructiveness and progress we call advancing wildfires, and from the isochronal variations of air quality that we can watch reflecting wind patterns and air movements in accelerated animated maps, showing the bad air that migrates and pool over the area I life. The even more ephemeral nature of these maps–they record but one instant, but are outdated as they are produced, in ways that fit the ecoystem of the Internet if also the extremes of the new ecosystem of global warming–the isochrones seem somewhat fatalistic, as they are both removed from human agency–as we found out in the weeks after the Lightning Siege of 2020–if they are the result of the extra urban expansion that has pushed houses to the borders of forests we haven’t ever faced the problem of maintaining and clearing in weather this dry: as a result, we are now burning underbrush that has lain like kindling, on the boundaries of the raging fire complexes, hoping to create fire lines and new perimeters able to forestall the advance of major fires, even as the smoke escapes from them and travels across the nation, far beyond the Bay Area.

While watching the movement of fires that them in inhabited areas like shifting jigsaw pieces that destroy the landscape across which they move. These marked the start of megafires, that spread across state boundaries and counties, but tried to be parsed by state authorities and jurisdictions, even if, as Jay Inslee noted, this is a multi-state crisis of climate change that has rendered the forests as fuel by 2017–for combined drought and higher temperatures set “bombs, waiting to go off” in our forests, in ways unable to be measured by fire risk that continues to be assessed in pointillist terms by “fuel load” and past history of fires known as the “fire rotation frequency.” When these bombs go off, it is hard to say what state boundary lines mean.

Fire Threat Risk Assessment Map, 2007

If San Francisco famously lies close to natural beauty, the Bay Area, where I live, lies amidst of a high risk zone, where daily updates on fire risk is displayed prominent and with regularity in all regional parks. These maps made over a decade ago setfire standards for building construction in a time of massive extra-urban expansion. But risk has recently been something we struggled to calculate as we followed the real-time updates of the spread of fires, smoke, and ash on tenterhooks and with readiness and high sense of contingency, anxiety already elevated by rates of coronaviurs that depended on good numbers: fire risk was seen as an objective calculation fifteen years ago, but was now not easy to determine or two rank so crisply by three different shades.

Fire Risk Map, 2007

When thunderstorms from mid-August brought the meteorological curiosity of nearly 12,000 dry lightening dry strikes from mid to late August 2020, they hit desiccated forests with a shock. The strikes became as siege as they set over three hundred and fifty-seven fires across the state, that rapidly were communicated into expansive “complexes” of brush fires.

We map these fires by state jurisdictions, and have cast them as such in policy, by borders or the perimeters we hope to contain barely grasp the consequences of how three quarters of a million acres burned up suddenly, and smoke from the cluster of fires rose in columns that spread across state boundary lines as far as Nebraska, and how fire complexes that spread across three million acres that would soon create a layer of soot across the west, eerily materialized in layers of GIS ESRI maps of environmental pollutants, while toxic particulate mater released in plumes of black carbon by the fires cover the state, rendering the sun opaque where I live, in the Bay Area, now Pompeii by the Bay as smoke at toxic levels blanketed much of the state.

They even more serious map, to be sure, was of fire spread: but the maps of air quality set the entire western seaboard apart from the nation, as if threatening to have it fall into the ocean and split off from the United States,–even if the burning of its open lands was more of a portent of things to come, they were a historical anomaly, lying outside the record of fire burns or air quality, if the poor air quality traced the origin of black carbon columns of smoke that would rise into the nation’s atmosphere.

Wilfire Today, AQI Map/Sept-15, 2020
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Filed under Active Fire Mapping, American West, Climate Change, data visualization, fires

On the Road and Off the Map: Maps for Self-Driving Cars in an Over-Paved World

Self-driving or autonomous cars promise a change in patterns of mobility more radical than any change in transportation.  While depending on maps, the maps made for self-driving cars are perhaps unlike any other:  not made or designed for human eyes. To be sure, we already drive in maps that we see ourselves moving along, in a mapped world as much as a real topography-and the internalized maps on our dashboards offer a basis from which it seems one barely has to move to imagine a machine-readable map of a self-driving vehicle–

–so that even our road signs can be obscured by the options that we have on the maps in our dashboards, and we are almost ready to offload responsibility for following the map onto the voices that provide directions, images that track our presence on the road, and put us in road maps, to permit replacing the landscapes through which we drive.

The increasing eventuality of assembling a machine-readable record of each edge of the road.  At the same time as roads are scanned, integrated with LIDAR imagery of the environment, and augmented with real-time feedback loops would provide a virtual 1:1 map of automative environments, in which cars could navigate autonomously–within parameters of speed limits, weather conditions, and oncoming traffic.  

Yet how intelligent are these “intelligent maps” by which self-driving maps integrate and position themselves in the space networks of roads?  As Shannon Mattern has argued, “With the stakes so high, we need to keep asking critical questions about how machines [are able] to conceptualize and operationalize space” that can recognize the human actors in space, and how the increased role of these networks of mapping serve as actants–shifting the networks of on-road behavior, or how, as Mattern puts it, “artificial intelligences, with their digital sensors and deep learning models” that perpetuate one image of space will “intersect with cartographic intelligences and subjectivities beyond the computational ‘Other'”  How will such maps, put differently, register people who also occupy the sidewalks, and the other cars on the highway (either as drivers or passengers), and how will they be effected by them?

The lack of a clear roadmap for self-driving cars notwithstanding, the eerily ghostly nature of LiDar views of streets, overpasses, and street side scenery seems to point up the absence of a sensory cartography of place. The maps for self-driving cars are not, it is true, for human subjects, and it is perhaps unfair to prejudge them or their selectivity. But despite the trust we are inclined to accord machines to reading space accurately and comprehensively, by synthesizing a total image of street conditions thatch replace the driver’s tacit sense of road conditions, as if they contain a greater precision than paper maps could hope to contain. We share amazement at the possibilities posed by mechanical sensing is, in a sense, pushed to new limits in the promises of self-driving cars, who have quickly gained multiple evangelists.

We already have cars able to signal their approach of the edges of traffic lanes, altering their human drivers of impending danger.  The promises of self-driving cars have generated increasing optimism in the United States and Japan, as the next generation of driving vehicles in a culture ready to embrace the new, perhaps because they promise the very possibility of constant motion in a country of speed.  But by removing routes of human motion and how humans move through road systems from direct intelligence, the maps that are being designed for autonomous vehicles to navigate the roadways of America and beyond suggest a new nature of space, as much as of transportation or transit:  and the maps for self-driving cars, while not designed for human readers, suggest a scary landscape rarely open to surprises and eerily empty of any sign of human habitation.

And what will even guarantee that the self-driving cars will not go off the roads?  The absence of human intelligence from the maps for self-driving cars creates a code-space that seems to depend on its interaction with human intelligence far more than its maps seem to register at first sight.  The simulated scenarios that have been created for such self-driving cars by engineers seem to seek to “provide a view of the world that a driver alone cannot access, seeing in every direction simultaneously, and on wavelengths that go far beyond the human senses,” but by nature depend on the ability to translate real-time scenarios in HD maps–as well as topological models–into the car’s actual course.

Waymo

For in promising to synthesize, compress and make available amazing amounts of spatial information and data sufficient to process the rapid increase of roadways that increasingly clog much of the inhabited world, they are maps for the age of the anthropocene, when ever-increasing spaces are being paved.  And although even after the arrival of promising “autonomous vehicles” from Tesla, which has introduced a new Autopilot feature able to maneuver in well-marked highways, and tests for urban driving by Uber, General Motors, and of course Google, the limited safety of relying only on sensors to navigate space in many areas, where vehicles are forced to integrate LIDAR, mid- and low-range radar, camera-based sensors, and road maps of real-time situations, and have difficulty calibrating road conditions and weather with the efficiency human drivers do.   The absence of a clear road map for their integration, however, is paralleled by the inability to synthesize contingent information in maps, which in their absence of selectivity offer oddly hyper-rich levels of information.

The notion of processing such comprehensive maps was far away when DARPA sent tout a call in 2003 inviting engineers to design self-driving cars that could navigate a one-hundred-and-forty-two-mile-long course in the desert, near Barstow, CA, across the desert to Prima, Nevada, without giving them a sense of its coordinates on a race-course filled with gullies, turns, rocks, switchbacks and obstacles–from train tracks to cacti–hoped to integrate GPS and sensors to create a car able to navigate space in as complete an image of road conditions as was possible.  If the rugged nature of these rigged-out vehicles recalled the first-run of a Mad Max film in their outsized nature paramilitary nature, designed as if to master landscape of any sort, they were so over-fitted with machinery were they with what seemed futuristic sensors that were tantamount to signage–

DARPAGCSa_04.jpg
darpa_cars.jpg
Poster 619x316

–to seem to wrestle with the fundamental problem of mastering spatial information that the new generation of autonomous vehicles have placed front and center.

The top-down attempt of DARPA to stage a race of autonomous vehicles, was intended to keep soldiers out of harm’s way in a military context.  But the attempt to generate a new sort of military vehicles raised compelling questions of integrating a range of spatial signs in their apparatus of machine vision, laser range-finding data, and satellite imagery, but suffered from an inability to take in environmental information–no cars completed the course, as it was staged, and the vehicle traveling the furthest went only seven and a half miles.  Even in a course that was located in the desert–still the preferred site, given the lack of weather conditions and better kept up road surfaces, to test most self-driving cars to minimize unpredicted external influence–the relation of car to world was less easily negotiated than many thought.

While the results of the DARPA grand challenge wasn’t immediately successful, although the basis it set for future collaboration between machine-learning and automotive companies in notions of remote sensing.  It placed front and center the problem remains of how to establish more than a one-dimensional picture of the road ahead of the car to navigate the road ahead most easily.  And by 2007, the Urban Challenge, invited autonomous vehicles to navigate streets of an urban environment in Victorville, Calif., against moving traffic and obstacles and following traffic regulations, in ways that lifted a corner on the mappability of the future of driverless cars, as if to throw pasta against th ewall in the hopes tht some of it would stick.  Although the new starting point of self-driving cars on a network of readable roads, equipped with recognizable signage, remains the most profitable area for development, the machine-readable road maps eerily naturalize the parameters of the roads in their content, and absent humans from their surface.  Despite the recourse to satellite photography and attempts to benefit from aerial views, the notion of a map for the autonomous vehicle was barely conceived.  But in the almost fifteen years since, the maps that are being developed for self-driving cars have grown into an industry of their own, promising to orient cars to machine-readable records of the roadways in real-time.

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Filed under 3-D maps, autonomous cars, HD Maps, machine-readable maps, self-driving cars