Modelling population density using artificial neural networks from open data

Adam Nadolny
Uniwersytet Przyrodniczy we Wrocławiu


This paper introduces the concept of creating a model for population density prediction and presents
the work done so far. The unit of reference in the study is more the population density of a location
rather than tracking human movements and habits. Heterogeneous open data, which can be
obtained from the World Wide Web, was adopted for the analysis. Commercial telephony data or
social networking applications were intentionally omitted. Both for data collection and later for
modeling the potential of artificial neural networks was used. The potential of detection models such
as YOLO or ResNet was explored. It was decided to focus on a method of acquiring additional data
using information extraction from images and extracting information from web pages. The BDOT
database and statistical data from the Central Statistical Office (polish: GUS) were adopted for the
base model. It was shown that the use of street surveillance cameras in combination with deep
learning methods gives an exam.

Słowa kluczowe:

population density, artificial neural networks, detection models, image information extraction

Pełny tekst:



Adamec, V., Herman, D., Schullerova, B., & Urbanek, M. (2019). Modelling of Traffic Load by the

DataFromSky System in the Smart City Concept. Smart Governance for Cities: Perspectives and

Experiences EAI/Springer Innovations in Communication and Computing,135-152.


Chen, P., Hsieh, J., Gochoo, M., Wang, C., & Liao, H. M. (2019). Smaller Object Detection for

Real-Time Embedded Traffic Flow Estimation Using Fish-Eye Cameras. 2019 IEEE

International Conference on Image Processing (ICIP). doi:10.1109/icip.2019.8803719

Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T., Abba, S., Vojtek, M., . . . Khoi, D. N. (2019).

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine

Learning Supported by Remote Sensing and GIS Techniques. Remote Sensing,12(1), 106.


Kong, X., Xia, F., Wang, J., Rahim, A., & Das, S. K. (2017). Time-Location-Relationship Combined

Service Recommendation Based on Taxi Trajectory Data. IEEE Transactions on Industrial

Informatics,13(3), 1202-1212. doi:10.1109/tii.2017.2684163

Kong, X., Li, M., Li, J., Tian, K., Hu, X., & Xia, F. (2018). CoPFun: An urban co-occurrence pattern

mining scheme based on regional function discovery. World Wide Web,22(3), 1029-1054.


Liao, Y., Yeh, S., & Gil, J. (2021). Feasibility of estimating travel demand using geolocations of

social media data. Transportation. doi:10.1007/s11116-021-10171-x

Panphattarasap, P., & Calway, A. (2018). Automated Map Reading: Image Based Localisation in 2-

D Maps Using Binary Semantic Descriptors. 2018 IEEE/RSJ International Conference on

Intelligent Robots and Systems (IROS). doi:10.1109/iros.2018.8594253

Pham, B. T., Phong, T. V., Nguyen-Thoi, T., Trinh, P. T., Tran, Q. C., Ho, L. S., . . . Prakash, I.

(2020). GIS-based ensemble soft computing models for landslide susceptibility mapping.

Advances in Space Research,66(6), 1303-1320. doi:10.1016/j.asr.2020.05.016

Pham, Q. B., Abba, S. I., Usman, A. G., Linh, N. T., Gupta, V., Malik, A., . . . Tri, D. Q. (2019).

Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall. Water

Resources Management,33(15), 5067-5087. doi:10.1007/s11269-019-02408-3Qin, Z., Li, Z., Zhang, Z., Bao, Y., Yu, G., Peng, Y., & Sun, J. (2019). ThunderNet: Towards Real-

Time Generic Object Detection on Mobile Devices. 2019 IEEE/CVF International Conference

on Computer Vision (ICCV). doi:10.1109/iccv.2019.00682

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized

Intersection Over Union: A Metric and a Loss for Bounding Box Regression. 2019 IEEE/CVF

Conference on Computer Vision and Pattern Recognition (CVPR).


Sachdeva, S., Bhatia, T., & Verma, A. K. (2019). A novel voting ensemble model for spatial

prediction of landslides using GIS. International Journal of Remote Sensing,41(3), 929-952.


Sassi, A., Brahimi, M., Bechkit, W., & Bachir, A. (2019). Location Embedding and Deep

Convolutional Neural Networks for Next Location Prediction. 2019 IEEE 44th LCN Symposium

on Emerging Topics in Networking (LCN Symposium).


Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., & Shirzadi, A. (2018). Novel

forecasting approaches using combination of machine learning and statistical models for flood

susceptibility mapping. Journal of Environmental Management,217, 1-11.


Smolak, K., Rohm, W., Knop, K., & Siła-Nowicka, K. (2020). Population mobility modelling for

mobility data simulation. Computers, Environment and Urban Systems,84, 101526.


Smolak, K., Kasieczka, B., Fialkiewicz, W., Rohm, W., Siła-Nowicka, K., & Kopańczyk, K. (2020).

Applying human mobility and water consumption data for short-term water demand forecasting

using classical and machine learning models. Urban Water Journal,17(1), 32-42.


Tang, K., Paluri, M., Fei-Fei, L., Fergus, R., & Bourdev, L. (2015). Improving Image Classification

with Location Context. 2015 IEEE International Conference on Computer Vision (ICCV).


Tenerelli, P., Gallego, J. F., & Ehrlich, D. (2015). Population density modelling in support of disaster

risk assessment. International Journal of Disaster Risk Reduction,13, 334-341.


Tsubouchi, K., Kobayashi, H., & Shimizu, T. (2020). POI Atmosphere Categorization Using Web

Search Session Behavior. Proceedings of the 28th International Conference on Advances in

Geographic Information Systems. doi:10.1145/3397536.3422196

Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2021). Scaled-YOLOv4: Scaling Cross Stage

Partial Network. arXiv [cs.CV]. Opgehaal van

Wang, J., Kong, X., Xia, F., & Sun, L. (2019). Urban Human Mobility. ACM SIGKDD Explorations

Newsletter,21(1), 1-19. doi:10.1145/3331651.3331653

Xia, F., Liu, L., Li, J., Ahmed, A. M., Yang, L. T., & Ma, J. (2015). BEEINFO: Interest-Based

Forwarding Using Artificial Bee Colony for Socially Aware Networking. IEEE Transactions on

Vehicular Technology,64(3), 1188-1200. doi:10.1109/tvt.2014.2305192

Xia, F., Liu, L., Jedari, B., & Das, S. K. (2016). PIS: A Multi-Dimensional Routing Protocol for

Socially-Aware Networking. IEEE Transactions on Mobile Computing,15(11), 2825-2836.


Yang, Q., Wang, J., Song, X., Kong, X., Xu, Z., & Zhang, B. (2015). Urban Traffic Congestion

Prediction Using Floating Car Trajectory Data. Algorithms and Architectures for Parallel

Processing Lecture Notes in Computer Science,18-30. doi:10.1007/978-3-319-27122-4_2

Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019). CutMix: Regularization Strategy

to Train Strong Classifiers With Localizable Features. 2019 IEEE/CVF International Conference

on Computer Vision (ICCV). doi:10.1109/iccv.2019.00612

Zheng, Y. (2015). Trajectory Data Mining. ACM Transactions on Intelligent Systems and

Technology,6(3), 1-41. doi:10.1145/2743025

Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2019, November 19). Distance-IoU Loss:

Faster and Better Learning for Bounding Box Regression. Retrieved from