INTRODUCTION

The primary objective of the operations aims at numerically and statistically expose the impact of urban greenery in the reduction of heating and pollution levels in the built environment, towards an informed and conscious organization of public street sections.

In this project we present strategies for point clouds reconstruction based on the automated acquisition and processing of Thermal and Multispectral imagery. The benefits derived from this methodologies consist in the implementation of customized low-cost data collection techniques, merging multiple datasets into a thermal-multispectral point cloud.

CONTEXT

Existing cities demands solutions to improve  energy efficiency while lowering the production of CO2 emission. Energy awareness is becoming increasingly important nowadays thanks to larger accessibility to Data, valuable in informing the decision making process for urban planners, supporting strategic choices for the incrementation of sustainable policies aiming at the reduction of environmental impact and footprint production. In the last 5 years, more than 50% of Barcelona pollution stations exceeded the mean average of PPM established by the European Union.

METHODOLOGY

The operational context has been identified along 11 nodes of Paseo de San Juan in Barcelona. This choice is justified by the morphological and functional characteristics of this particular street, presenting strong differentiation of its section along its path, as presence of greenery, mobility and pedestrian paths structure.

Another relevant key aspect is the orientation of this street, mostly uniform, along the NW/SE axis. Those specific conditions have been considered valuable at the moment of comparing the different sections one with another.

TECHNOLOGY

The proposed workflow is based on automated robotic collection of multispectral images through the implementation of a ground rover equipped with thermal and multispectral cameras.
This solution allow us to remotely gather images, programming the most efficient path to maximize the amount of data collection and minimize timing of the overall operation, in comparison with other existing processes.

The characteristics of the rover allow the machine to move nimbly along the different road paving, without interrupting vehicular or pedestrian mobility.

Data processing is developed through a specific customized method, generating one single point cloud, storing RGB, NDVI and Thermal Data over the same geometrical organization.

This method reconstruct and merge imagery into geometrically structured data, reconstructing the physical street environment, allowing to visualize thermal and NDVI values from the built and green envelope. As a final stage, the extracted 3D geometric model is saved as an industry standard file format for data interoperability.

RESEARCH FIELD
URBAN ENVIRONMENT ANALYSIS

PROJECT TITLE
BCN Mapping

YEAR
2018

PARTNERSHIP
Barcelona Regional

MAPPING GREEN CORRIDOR

Highlight of the 11 selected points mapped along Paseo de San Joan in Barcelona.

CASE STUDY

P5 – PASSEIG SAN JOAN: Rossello-Corsega
This area is a portion of the Passeig de San Joan, located in between Carrer Rossello and Carrer Corsega. The area is characterized by a very small terrain inclination (2-3%). The buildings surround the two sides of the area and the relatives facades are for the majority covered by the greenery. The mapping strategy sees the robot moving along the area following the outer perimeter facing the camera towards the inner part of the area. A second turn is done moving the robot in the central part of the area while the cameras look towards the outer part of the area. This double path allow to minimize the blind spot of the camera and to obtain a much more reliable quality of the data. 

image-(1)-(1)

SURVEY DATA
Day: 21-06-18
Number of images: 1,370
Ground resolution: 1.62 cm/pix
Camera stations: 1,358
Tie points: 1,113,597

CAMERA INFORMATION
Type: Fisheye
Resolution: 1280 x 960
Focal Length: 3.98 mm
Pixel Size: 3.75 x 3.75 μm

OUTPUT
Pointcloud : 5,469,416
Format : PLY textured
Coordinate system : Local

CAMERAS OVERLAP

This indicator express the amount of neighbor picture for every picture collected. In this case the blue color reflects a good data collection process.

SPATIAL DATA
3D pointcloud are useful for their spatial information that allow to measure distances, extensions and volumes. In urban environment application they results extremely useful for the digital reconstruction of plan layouts and facades.

NDVI ANALYSIS
Here follow several maps of the analyzed area. Using NDVI we separate greenery channels corresponding to different plant’s health condition.

NORMALIZED CLOUD
By spatially normalizing NDVI and Thermal cloud a common protocol is defined. In this case the the comparison is done with a sampling grid of 0.38×0.38×0.38 meters. Thanks to this process we can overlap different layer of information relative to the same cloud and create cloud to cloud comparison thanks to the common sampling base. Using thermal imagery is also possible to study the correlation between greenery and thermal relief that trees and vegetation create.

DATA CORRELATION
An automated data visualization process is triggered starting from the pointcloud file. Crossing values of temperature and greenery a mathematical correlation is generated. This allows to detect where the thermal relief is bigger thanks to a synthetic map that is produced as output. The analysis is iterated over two samples of data: the raw data containing all the thermal spectrum and on a filtered portion that correspond to the warmer areas.

GLOBAL COMPARISON

After executing the reconstruction process for all the selected points, a comparison in between points is permitted. Several layer are compared: greenery amount (%), average temperature (luminosity value) and thermal relief (difference between greenery temperature and building temperature).
As a synthetic conclusion, a lower temperature is recorded in the area with higher amount of greenery, in the opposite hand, temperature peaks are visible where greenery  is not diffuse and isolated.

GREENERY – BUILT (%)

Comparison between greenery and buildings
(threshold of 0.25 in the NDVI index)

AVERAGE TEMPERATURE (LUMINANCE)

Average temperature expressed as luminance value derived from thermal imagery.

AVERAGE TEMPERATURE (LUMINANCE) COMPARED WITH GREENERY %

This map describes the effect of greenery in relation to temperature. In the areas with higher greenery (P10) we can determine a lower average temperature and viceversa (P8,P9).

PARTNERS

icon-noum-200x150
B-REGIONAL