Dear Anupam, Thanks for the response. The main bottleneck in this process is having access to high-resolution imagery. The codes are all open and many groups seem to be using them in places other than India. If you come across any useful applications, we would be open to exploring those opportunities.
On the second point, yes, GPS-based data would be much more useful, but again, that's not a free dataset. The Google Distance API actually uses cell signals to gather congestion-related information, but it's not a free dataset, and over the last three or four years, they've really increased the price tag for using that API. In our emissions distribution step, having granular information is much more useful -- granular in space and time. Using an image provides a static dataset on where vehicles are on the road and also where parking lots are (space dimension). This, again, is a proxy that could align with just road density, which is the theory we've been using for the longest time. If we can add another layer of vehicle density to it, the spatial analysis of emissions at higher resolutions, especially in big cities, will improve, along with some of our other analyses. Hoping others on the list can add to this discussion. With best wishes, Sarath -- *Dr. Sarath Guttikunda* *https://urbanemissions.info <https://urbanemissions.info/about-ueinfo/>* On Tue, Jul 1, 2025 at 9:35 PM Anupam Sobti <anupamso...@gmail.com> wrote: > The constraint here might be availability of usable high resolution > imagery. I believe Google prohibits use of its imagery and the imagery that > these tutorials use are not available in India. Some people use night time > lights overlayed on street networks as a proxy for vehicle density but I > think GPS based google data would be more reliable. > > Regards > Anupam > > On Wednesday, June 18, 2025 at 12:18:08 PM UTC+5:30 Sarath Guttikunda > wrote: > >> In our group, we frequently perform extensive numerical analysis, >> particularly to understand emission intensities, both spatially and >> temporally. The spatial understanding of emissions is a significant >> component of our work, as it is crucial for accurately placing emissions >> before they are modeled and concentration maps are generated. >> >> This process is technically known as the "gridding of emissions." For >> example, if we know there are one thousand trucks operating in a city, each >> traveling one hundred kilometers a day, we can multiply these figures by an >> emission factor for a specific pollutant to determine the total emission >> intensity of trucks moving within the city's airshed. The question then >> becomes: how do we distribute these emissions into various grids for a >> city? We typically work with one-square-kilometer grids on average, and you >> can see some examples below. >> https://urbanemissions.info/india-air-quality/india-ncap-city-airsheds/ >> >> One of the proxies we use for trucks is highways. The assumption is that >> most trucks will travel on highways and spend the majority of their time >> there. Therefore, we assign a higher weight to the grids that intersect >> with highways. We also incorporate other layers of information with >> additional weights. For instance, industrial hubs, commercial hubs, malls, >> and markets are places where these vehicles are likely to go and spend some >> time. This methodical approach generates various weighting functions, and >> once we have the emission intensities, it produces a gridded emission file. >> So far this method of madness works and we have a good understanding of how >> the layers are behaving with some plus minus. We have an example tool to >> play with this method -- https://urbanemissions.info/tools/ >> >> We aim to improve this process. One of the layers we introduced in the >> past was speed information from the Google Maps API. We can download speed >> data, which also indicates congestion times. We utilized this as another >> proxy to understand where and for how long vehicles spend time, and >> accordingly, assign weights. See example image for Mumbai here - >> https://urbanemissions.info/india-apna/mumbai-india/ >> >> A new approach we want to explore, given some recently available >> information (and algorithms), is vehicle density. This would again be a >> static input. For example, if you take a satellite image and apply an >> algorithm, you could determine how many vehicles are visible within each >> grid. Because this is a static image for a specific time, we cannot use it >> as a layer for all-purpose gridding. However, it would serve as an >> additional layer of information that accurately reflects what is happening >> on the ground. It could also be used to extract information about official >> and unofficial parking lots where vehicles spend a significant amount of >> time on a given day. This would allow us to extract valuable insights. >> >> There are many online examples of this being done using geostationary >> images in Europe and the United States and most of them require an image >> and rest seem to work (take it with a pinch of salt -- non-it-person >> speaking). >> >> https://www.linkedin.com/posts/giswqs_geoai-geospatial-ai-activity-7309216621997281280-46yC/ >> >> https://up42.com/marketplace/analytics/detection-cars-oi >> A commercial portal -- but seems to do exactly what we want at a price >> >> So, the question to the group today is this: If there is a grid file, let >> us say for Bangalore -- has anyone done anything similar to create a >> vehicle density map, regardless of the vehicle type? or have any ideas >> on how to approach this for Indian cities? >> >> Please keep in mind that the ultimate goal is not to identify individual >> vehicles or count vehicles from traffic cameras. The focus is on a >> static image: if we have one, can we, or has anyone, worked on creating a >> vehicle density map from it? >> >> Any sights into making an example and hopefully scaling it up is >> appreciated. >> >> Looking forward to the follow ups. >> >> With best wishes, >> Sarath >> >> -- >> *Dr. Sarath Guttikunda* >> >> *https://urbanemissions.info <https://urbanemissions.info/about-ueinfo/>* >> > -- > Datameet is a community of Data Science enthusiasts in India. 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