Greg and the officer decided to continue throughout the neighborhood to collect more panoramics while the rest of the team finished the final few mapping flights. The panoramic images of the whole neighborhood took only a few hours (and a few batteries), compared to the days of dozens of batteries required by the traditional approach. At the end of it all, Greg had a computer filled with 10,000 photos, 50 Gigabytes in all, a disaster scene turned into pixels.
It took Greg another 2 days of processing (so much for his new relaxing career move) the thousands of photos and reaching the limits of his home laptop. He had never worked with such massive datasets before and had to call in technical assistance from the San Francisco office of Pix4D. With their help, the 2D dataset was done….all while additional panoramics kept rolling in from the officers still out in the field in other hard hit neighborhoods.
The next step was to visualize the data for Sonoma county office, who was leading the efforts on the ground. The problem was the end maps were several gigabytes and would need to be broken down into tiles. So, Greg called up the Mapbox office in San Francisco. He didn’t actually know anyone this office, but still they answered his plea for assistance and got right to work. They too wanted to help. Communities and their businesses were coming together. Within 24 hours, they had a map viewable online with streets and even the individual addresses of each home, or where each home had been. The Mapbox team was also able to overlay the panoramic images from the neighborhoods in which they had been taken.
You can see these maps here (camera icons depict 360 panoramics).
What they show is a series of images stitched together to show, in both 2D and 360 degree detail, the neighborhood in extreme high resolution. By connecting the maps, you (and all the public agencies) can see each burn in detail but also context. Ultimately, the maps were made public so that residents could look at their neighborhood before the road blocks opened up.
At the same time, the Hangar team out in Austin was working on a custom tool to help out the cause. In a day, they had created an interactive map of the panoramics taken by the officers out in the field, while Greg was back at home processing. They also might have inadvertently created the next big tool in disaster management. Their mapping approach is a simple, elegant design with pins dropped in the locations of each 360 panoramic. Moreover, as the viewer moves the scene, there is a field of view that changes on the topographic. The shifting field allows the user to easily locate the direction you are looking for clear frame of reference. It is often said that necessity is the mother of intervention, just as often it is hardship.
The end result, similar to Mapbox map, is a stunning perspective of the fire damage and loss of property. In fact, some of the imagery was so high quality the public agencies didn’t want it released until a few hours before the neighborhoods reopened because the fire safes of residents were clearly visible in some burned homes. The sheriffs were worried about looters seeing the safes.
In and of itself, what Greg did proved extraordinarily useful. He will be called again (there will be more emergencies) and he will be useful again. If he had any doubts about leaving academia with its passive aggressive brawls and anticlimactic victories, those doubts evaporated as he was sitting at the side of one of the fires with his computer. It evaporated when he saw the local agencies planning their responses with the data he helped gather. It wasn’t why he had started to work with drones, to help the community, and yet here he was, covered in sweat and breathing in smoke, absolutely fried from the persistent lack of sleep, and yet as sure as he had been in his career that he was doing the right thing.
But there was a turn in the story. When Greg finally handed off the work to the county GIS team, he did not sleep well. He was overtired. He had just started a business. He smelled like ash in a way that wouldn’t wash away. His mind was spinning. He was still trying to get his daughter potty trained. Ordinary life and this extraordinary moment needed to coexist. But there was more. His old life as an ecologist kept leaping up out of the dark recesses of his mind like a wild animal, a restlessness. What if, he imagined, he could pull together not just his own drone images onto a single map, but also other peoples’ drone images? What if he could coordinate people with drones around California, or around the world, to all take the kind of images he was taking and then to submit them to a central data site? Nearly a million drones are flying around the US alone right now. A MILLION! If even a tiny subset of those drones could be marshalled in some coordinated way they could see what is otherwise impossible to see. Then, he thought about Houston. In Houston, a huge part of the problem of the responding to the hurricane was being able to link up the images and drone footage from across the city. Many independent observations were being made, but they were herky jerky, the high resolution equivalent of a film flashing at the wrong speed and without space or narrative. They couldn’t be made to make sense. The same for Puerto Rico, only worse. And again for the recent shootings in Las Vegas. But this, he sat up and thought, could be solved. And it wouldn’t take machine learning or artificial intelligent or any other hot buzzwords floating around Silicon Valley venture capital firms right now. All it would take was an iPhone, an app and a DJI drone from the local Best Buy, or rather thousands and thousands of each of those. What it would take was a community.
Which is when he called me.
When Greg called he was on edge, that old edge of the discovery combined with the new edge of being able to help people in a way he never imagined. He wanted to join forces to combine what we do in my lab (to engage the public in citizen science in order to speed up discoveries around the world) with what he has begun to do. He wanted to use the public in a coordinated way to see. “Every major discovery,” I heard a fast talking Greg saying, has been about seeing more. “With drones we could see so much more. We could study global patterns in biological diversity. We could study the outbreak of pests.” We could study, I said in response, “the tides,” and “sea level rise.” “Or forest loss,” Greg said. Or forest recovery, one of said. Or deaths of trees due to beetles. We could see not like a bird flying, or a hawk hovering, but instead like thousands and thousands of hawks and what was more the ways in which we could see could be coordinated to particular moments.
Ultimately, what Greg hopes to do is to crowdsource the equivalent of Google Street view but from the sky. He wants to issue a call to action for drone pilots. These pilots could be public agencies, companies, or volunteer citizens depending on the circumstance (e.g., citizens shouldn’t fly in disaster zones). Locations could be randomly chosen or could be targeted for for specific questions and needs. While there have been other companies doing this for drone-mapping jobs (essentially; Uber for drones), their approach is commercial and elaborate, a for profit empire where a village might do. Such a village would be a huge aid in future fires, floods, and other disasters, but before the key moment when a team of drones would be needed in such an urgent way, before the emergency, one would want to test the team of citizen drones in a place that required coordination but was not life or death. And what better thing to test than to once again return to the ecological questions with which Greg started. A simple test that takes advantage of the data drones provide, data that are an order of magnitude in resolution than anything derived from satellites and less impacted by cloud cover. Greg and I decided, on the phone, to start with a simple, and yet important test. We would see if we could bring together the drone village to ask, how are leaves changing in the autumn?
It is actually surprisingly hard to capture the timing and details of the changing leaves of plants in the fall (or in the spring for that matter). Yet, knowing when the leaves turn, fall, and bud anew is key to understanding the impacts of climate change, urbanization and even biodiversity on the growth and future of forests and grasslands. The need for such data is real. Also, it being the middle of October, with Halloween coming up, the timing is perfect right now. We could summon the community of drones and with them map, all at once the status of the leaves across the United States, around the world.
And so we begin the Fly4Fall experiment. And you can help. You can take part in the test right now and for the next few weeks. If you have a DJI drone and want to participate click to see how you can be involved here at fly4fall.com.
Meanwhile, Greg has finally gone to sleep. His desk remains covered in thumbdrives from the mapping project. His daughter is mostly sleeping, though still not using the potty regularly (well, she’ll pee at least). Out not far from his house the fires are finally getting under control from the heroic efforts of fire crews. And even as all of this is happening, you can look at the images Greg compiled. You can see the fires, but also their scale. You can walk into each one. You can imagine the heat. You can imagine too the stories of the peoples’ whose houses have been lost, but also the story of the community that came together to help after the fires had gone through, a community assembled rapidly out of a need, a community that Greg hopes, that we now hope, can be built upon to react even faster and more effectively in the future. There will be more fires. There will be more floods. But there will also be, we have to have, an ever stronger community able to respond to them, a community in which the police, ecologists, and whoever else is needed use every tool at hand and some that, in those moments, are created anew, to respond.