Hello, good day. I'm glad to present our work named UVLens,
which is urban village boundary identification and population estimation leveraging government open data.
My name is Longbiao CHEN, assistant professor from Xiamen University, together with my collaborators.
In this very difficult situation, I hope you are all doing well and taking good care.
Now let me start the presentation.
Here is an image of a typical urban village in Shenzhen, China.
Urban villages refer to the residential areas lagging behind the rapid urbanization process in many developing countries.
For example in China, although the urbanization rate is close to 70%, a lot of old cottages remain and are scattered in the city.
The residents, sometimes called "the ant tribe", are usually low-income and transient inhabitants,
such as migrant workers in nearby factories, and low-income university graduates.
Urban villages provide them with affordable housing conditions close to public transportation at a relatively low price.
However urban villages also bring several social and economic programs for urban authorities.
Urban villages are generally overcrowded, filled with illegal apartments and family rentals.
The living standards in urban villages are relatively low, and the lack of public health systems
makes urban villages vulnerable to infectious diseases, such as COVID-19.
Therefore it is essential for other authorities to know about the basic situations in these areas.
The first key question is to know where and how large in the city are the urban villages,
or to identify the urban village boundaries.
The second question is knowing how many houses and how many people are living in these areas,
or to estimate the population of urban villages.
Traditionally urban authorities usually resort to field surveys and demographic census
by sending out investigators to urban villages.
However, these methods are usually time-consuming and human-intensive.
Besides, as urban villages are continuous being renovated, these statistical data are usually outdated,
and may not reflect the state-of-the-art.
In this paper, we propose a new idea to identify urban village boundaries
and estimate their population leveraging heterogeneous urban data.
Specifically, we resort to the following two categories of open data.
The first category is infrastructure data, including high-resolution remote sensing imagery,
city-wide street view imagery, and also points of interest data.
The second category of data is instead collected by ubiquitous devices
from large scales of crowds, including taxi GPS trajectories, bike-sharing system records, and so on.
The proposed framework is shown as follows including two phases.
Now let me introduce the first phase, i.e., urban village boundary identification.
In this phase, our objective is to identify urban village boundaries from remote sensing imageries.
Although such kind of image classification and segmentation tasks are not difficult,
when directly applying existing algorithms to our task, we face the following two issues.
First, if we clip large remote sensing imagery into fixed-size imagery patches,
the resultant urban village boundaries might be incorrect, leaving lots of spaces alongside the current borders.
Second, these algorithms tend to classify similar function-areas, such as old residential buildings, as urban villages.
In a word, we need to incorporate more information to achieve better boundary identification results.
In this work, we propose to fuse crowdsensing mobility data with remote-sensing imageries
to improve the boundary identification accuracy.
Specifically, in order to avoid incorrect boundaries, we extract road networks from taxi trajectories,
and generate patches to maintain the integrity of urban villages.
Second, in order to avoid identification errors, we fuse the bike-sharing usage data to distinguish
urban villages from residential areas, as bikes are usually not allowed to enter residential areas.
Here, we show an illustrative example of urban village boundary identification results.
We can see that with the help of bike usage data, we can safely avoid the residential areas from being identified as urban villages.
Also, by leveraging road network-guided patches, we can identify the urban village without missing boundaries.
Now let me introduce the second phase, urban minute population estimation.
We consider the factors that impact the urban village population from the following two aspects.
The first factor is living space, which can be estimated by counting the number of houses from satellite imagery.
The second factor is human activity intensities, which can be estimated from bike usage and POI hotness.
Therefore, our basic idea is like if we see an urban village with crowded houses and frequent bike usage,
we can infer that there might be more population in this urban village.
Specifically, we first extract the household capacity feature from different kinds of houses with different sizes,
including residential houses, rental apartments, residential apartments, and old houses.
An empirical formula is used to estimate the basic population feature.
We then refine the population estimation result with human activity features to reflect the overcrowded situation of urban villages.
Specifically, we derive weekly activity intensity from bike usage, and also the commercial hotness of POIs.
Finally, we evaluate the performance of our approach using real-world data collected from Xiamen, China.
We use satellite imagery from google earth, taxi and bike trajectories, along with POIs in different categories.
Here is a glance at the data we used.
We now present some interesting findings in our evaluation. The first one is the vanish of an urban village called Dongzhai.
We can see that since 2015, the urban village was shrinking gradually, and its surrounding road network was changing dynamically.
We also present two negative cases here.
On the left, we show an urban village that is not detected by our method,
since it is a relatively small urban village in a big patch, making it difficult to be detected.
On the right, we show an urban village with a wrong population estimation.
The reason is that the urban village has become one of the tourist attractions,
and its human activities are largely affected by tourists instead of reflecting the residential population.
Recently, we have also deployed our system on Xiamen Urban Big Data Security Open Platform
to provide information services to urban authorities and citizens.
Finally is our takeaway message.
In this work, we present a fine-grained and low-cost approach to urban village identification and population estimation.
We propose a heterogeneous data fusion framework leveraging infrastructure and crowdsensing data.
We have performed real-world evaluation and deployment in Xiamen, China.
This is Longbiao CHEN from Xiamen University. Thank you for your attention.