1. 3D city models for urban population estimates Lívia Rodrigues Tomás November, 2009

2. ABOUT ME Architecture and Urbanism MsC in Urban Planning Ph.D candidate in Remote Sensing - INPE  Advisors:  Dra. Leila Fonseca e Dra. Cláudia Almeida

3. INTRODUCTION Interest Area  cities Urban environment  growing in a fast and uncontrolled way – continuous changes Study Method  monitor the spatial dynamics of cities Remote Sensing  source of data TO PLAN  updated data 3D Models  POPULATION ESTIMATES

4. Demographic census The most important tool to characterize the population of a given area; In Brazil – CENSUS is accomplished every 10 years; Although the census data present detailed information on the urban population, data on land use patterns and urban growth dynamics are still needed to effectively monitor the cities continuous changes.

5. Population Estimates A solution for acquiring information in inter-census periods is the usage of alternative sources for demographic data assessment.

6. Proposal An alternative method  Population Estimates Remote Sensing  Employed Methodologies  2D data  HYPOTHESIS  inclusion of height data (Z) will enable a refinement of population estimates initally obtained by 2D data

7. Proposal The lack of updated population data 3D Models will refine the 2D population estimates Generate 3D Models (IKONOS + LIDAR) Uberlândia - MG Refuse or Confirm Hipotesys methodology analysis STUDY AREA HIPOTESYS PROBLEM

8. GENERAL GOAL Generate 3D city models, obtained by means of active (LIDAR) and passive remote sensors (IKONOS), meant to support the estimates of urban population.

9. STUDY AREA

10. Study Area Source: PMU (2007)

11. Study Area Source: PMU (2007)

12. INPUT DATA Digital ortophotos at a 1:2,000 scale Laser scanning (ALTM 2025 Optech) A stereo pair of high resolution imagens from IKONOS Vector Data Non spatial data.

13. IKONOS

14. DHM (Digital Height Model)

15. Hipsometric + IKONOS image

16. Hipsometric + IKONOS image

17. Data Acquisition Vector Data IKONOS DSMDEM nDSM Height Segmentation Classification Residential Buildings Buildings 2D Buildings 3D Orthoimage LIDAR DSM METHODOLOGY

18. calculation of the residential buildings volume conversion of volume data into cumulative residential surface; population estimates based on the residential surface; cross-validation of results. METHODOLOGY

19. 3D city models for urban population estimates Lívia Rodrigues Tomás livia@dsr.inpe.br November, 2009