jcmeunier77 / house_3d_api

House_3D_API

1. Purpose and project objectives

Purpose

• To model a house in 3D from lidar satellite images (geoTIFFs file) by only entering a home address

Pitch of the project

Put yourself in the shoes of a company, active in the Geospatial industy and whose purpose is to use lidar satellite image data to launch a new branch in the insurrance business. With this respect, the company needs you to build a solution with these data to model buildings in 3D with by only entering a home address.

What is LIDAR ? LIDAR stands for Light Detection and Ranging and is a remote sensing method used to examine the surface of the Earth. LIDAR is a remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth. The device will illuminate a target with a laser light and a sensor will measure the reflection. Differences in wavelength and return times will be used to get 3D representations of an area.

Objectives

• Consolidate the knowledge in Python, specifically in : NumPy, Pandas, Matplotlib
• To be able to search and implement new librairies
• To be able to read and use shapefiles
• To be able to read and use geoTIFFs
• To be able to render a 3D plot
• To be able to construct the project with object-oriented programming (OOP)
• To be able to implement the whole project - and make it functioning - through an API
• To be able to present a final product

Features

Must-have

• 3D lookup of houses

Nice-to-Have

• Optimize your solution to have the result as fast as possible.
• Better visualization
• 3D Belgium's monuments i.e church, etc.

2. The project

Working plan and steps

1. Research

• Research and understand the term, concept and requirement of the project.
• Discover new libraries that can serve the project purposes
  • geopy - convert physical addresses to Geographic locations
  • folium - plot address on a map
  • rasterio - read and write GEOTIFF format file
  • pyproj - performs cartographic transformations and geodetic computations
  • rioarray - rasterio xarray extension (xarray - working with labelled multi-dimensional arrays)
  • matplotlib (mpl_toolkits.mplot3d Axes3D) - plot 3D objects on a 2D matplotlib figure
  • open3d - a modern library for 3D data processing
  • shapefile - provide read and write support for Shapefile (.shp) format
  • geopandas - make working with geospatial data in python easier and extends the datatypes used by pandas to allow spatial operations on geometric types
  • earthpy - make it easier to plot and work with spatial raster and vector data using open source tools
  • fiona - provide read and write support to real-world data using multi-layered GIS formats and integrates readily with other Python GIS packages such as pyproj and Shapely
  • cartopy - designed for geospatial data processing in order to produce maps and other geospatial data analyses
  • shapely - provide support for manipulation and analysis of planar geometric objects
  • gdal - translator library for raster and vector geospatial data formats

2. Data collection

• DTM file for Flandre including Brussels
• DSM file for Flandre including Brussels
• Shapefiles with cadastral maps and parcels

3. Python procedural programming

• Programming python command and function for each step of the process flow, i.a.:

  • Returning CRS coordinates (epsg: WSG84 and 31370) from address input command
  • Identifying right .tif or shp. from CRS coordinates
  • Locate property from CRS coordinates
  • Cropping .tif file with self-made shapes (e.g. polygon) or with cadastral .shp file geometries
  • Constructing 3D point cloud files from cropped .tif file
  • Using various algorithms (i.a. poisson, pall-pivoting,...) to generate 3D meshes from point clouds or to improve 3D visualization

• Testing and fine-tuning code for improving readability, effacy and/or execution speed

see notebooks with single commands and functions, for further details

4. Python object-oriented programming

• Deploying each single procedural commands into four coherent and consistent objects :
  • a_addres_to_crs.py : returns CRS coordinates from input address
  • b_crs_to_tif.py : uses encoded CRS coordinate to locate the right .tif or .shp file
  • c_file_to_pix.py : return 3D visualization from cropped .tif files
  • d_target_to_map.py : save a folium map as .html file from CRS coordinates
• Constructing a 'meta' object for dispatching commands to the lower level objects
• Designing a Flask API for linking an .html interface to project objects
  • with 2 routes : one for opening the address form (see next section) and one for render folium map .html template
  • rem: the 3d visualization being open as a separate open3D object

5. Designing user friendly interface with form and buttons

• Using CSS/HTML adapted from available bootstrap templates and codes
• Form :
  • for encoding address
• Buttons :
  • 'Locate building on map' : opens folium .html map in new window
  • 'View 3D reconstruction' : opens the open3D object in a separate object

6. Wrapping up things

• Linking the .html interface with the API with some javascript code
  • by clicking on the interface buttons the interface communicates with the API for lauching the desired commands
• Linking each blocks of the project and ensuring an efficient communication between them

Project structure and process flow

to incorporate : flowshart with process flow

Project output

Screenshot of the .html interface

Screenshot of the folium map

Screenshot of the 3D reconstruction

Pending

• To host the data on an online server.
• To develop web application allowing client to type in any address in Belgium to view 3D houses.
• To obtain latest LIDAR images so that new houses after 2014 can be rendered.
• To incorporate cadastral plan of each property in the 3D rendering so that only the requested house is rendered.
• To explore other 3D plotting libraries