Geo-spatial Research: Transition from Analysis to Synthesis

Editor's Notes

• #4: Various Street View datasets are now available online. And perhaps the most popular one is Google Street View, which has covered more than a hundred countries to date. And interestingly Street View provides researchers with a new way to observe neighborhood.
• #5: Check Steve Seitz and U of Washington Phototourism Page
• #8: So why street addresses are important, why we need adequate mapping. Let me ask you, how many of you had a unique address up to your house or flat number, back home? According to geocoding companies, 75% of the world is unmapped and UN says 4 billion people are invisible because of that. This lack of addressing is even worse in disaster zones, for example in Haiti Earthquake, Humanatarian Openstreetmap community started remotely mapping the disaster area in 48 hours, and mapped adequately in 6 months for NGOs and aid agencies to use. But are curves on a plane enough to be a map? How do we refer to places, how do we define locations? Are those maps complete without any labels?
• #10: Capture, analyze, act
• #11: Capture, analyze, act
• #12: Capture, analyze, act
• #13: Capture, analyze, act
• #17: Socio Economic and Physical environment,
• #20: Cities are physical too: Using computer vision to measure the quality and impact of urban appearance N Naik, R Raskar, CA Hidalgo - American Economic Review, 2016 - aeaweb.org Streetscore-predicting the perceived safety of one million streetscapes N Naik, J Philipoom, R Raskar… - Proceedings of the …, 2014 - openaccess.thecvf.com
• #27: Capture, analyze, act
• #29: So why street addresses are important, why we need adequate mapping. Let me ask you, how many of you had a unique address up to your house or flat number, back home? According to geocoding companies, 75% of the world is unmapped and UN says 4 billion people are invisible because of that. This lack of addressing is even worse in disaster zones, for example in Haiti Earthquake, Humanatarian Openstreetmap community started remotely mapping the disaster area in 48 hours, and mapped adequately in 6 months for NGOs and aid agencies to use. But are curves on a plane enough to be a map? How do we refer to places, how do we define locations? Are those maps complete without any labels?
• #30: Welcome to our presentation of Robocodes! Hopefully we’ll jump-start the conference with an impactful presentation to keep you pumped up for the rest of the week. Today I will introduce you our approach for generative street addressing from satellite imagery. This project is developed at Facebook and MIT Media Lab.
• #32: Will people use street Signs $40 per sign City counsel used approved Naming streets is politically unsavvy 20% of first time deliveries undelivered $300M/year loss because of inadequate addressing Need an addressing system for emergency, businesses, and residents
• #37: On the bright side, we don’t need to re-invent addresses, as there are many addressing schemes already organically being developed and experimented throughout the history. Within these addressing schemes, London postal code system caught our attention with its linear and hierarchical features. Also other addressing schemes guided us for some design choices that I will discuss next.
• #38: Our addresses consist of four alphanumeric fields, hierarchically designating larger areas in order. It resembles real world addresses, containing house number, street name, city, state and country. The region naming scheme is inspired from the London scheme in the previous slide. The first letter is dedicated for orientation from the downtown (north, south, west, east) and the second letter cues the distance from the downtown. So the yellow house on the left is in region WB. Then, within a region, the roads are named according to their orientation and order. Finally, the house numbering is based on the distances along and from the road.
• #39: We followed some design principles for making our system both efficient, and user friendly. I won’t go through all, but for example, the storage should be supporting forward and inverse geoqueries, should be extendible for address changes, should be compressible, etc. On the other hand, the linear and hierarchical properties of the scheme supports user integration, intuitiveness and memorability.
• #40: Back to our pipeline, we start by extracting road masks from satellite images by deep learning, then we find the individual road segments from those predictions. We create regions from the road graph, and label the parcels by distance fields from the roads.
• #41: Focusing on developing countries, the urban structure in those satellite images are not easy to detect and organize. Also there are different weather and illumination conditions per country.
• #42: Our annotaters created binary road masks from satellite images, on zoom level 19 satellite images. Then we train a SegNet model on those images, and we are able to learn road predictions as shown here, with 72.6% precision and 57.2% recall.
• #43: After we have the predictions, we threshold and thin the roads to find the skeleton. Then we use orientation bucketing to find individual road segments, which creates the base for streets.
• #44: After we have the road segments we create a road graph where nodes represent intersections and edges represents road weighted by their length. We partition the graph for maximum inter minimum intra connectivity of clusters, using normalized min cut. We also use some urban rules to limit the clusters.
• #45: After the regions are created, we mark the densest are as the downtown, and bucket other regions based on their orientation with respect to the downtown, in south, north, east, and west directions. After the regions are named, we find the two major axes of the roads within each region, and name the roads in each region based on their orientation and order.
• #46: Finally, we put meter markers on the roads by pixel distance, and we compute distance fields to create the block offsets for the house numbering. The gradient in the left image shows those offsets
• #47: As defined in our motivation, we want to locate and connect the invisible 4 billion, right? So we tested our system on unmapped developing countries. The map coverage improved up to 80% in some areas,
• #48: In conclusion, we presented a robust addressing scheme, a deep learning and graph partitioning approach for street extraction, and ready to deploy maps and tools for easy geoqueries.
• #49: Capture, analyze, act
• #50: Capture, analyze, act
• #52: Capture, analyze, act
• #57: To partition the road graph we experimented with different algorithms as newman girvan and modularity based partitioning. Since there is no clear definition of regions, our domain experts evaluated the success of region creation by using urban rules as geography, population, and road distribution. We also experimented with partitioning the dual of the road graph and traditional image based approaches as mean shift and super pixels
• #58: As an output, our system generates .osm files with roads. However since it does not make sense to output addresses for each five by five area, we compute the meter marking and offsetting on the fly. We modified the id-tool of MapBox, which is also the tool that openstreetmap uses, to integrate that last mile computation. We also developed an rtree extension for efficient querying. Lastly, as you can imagine, our addresses needs to be evaluated by real users: so we developed a mobile app to enable self navigation with our robocodes. In our user studies, we observed that using our smart addresses decreased the arrival time of the agents by 21.7 percent.