sandboxed-api/oss-internship-2020/gdal

GDAL Raster GeoTIFF Workflow

Build Tools: CMake/Ninja
OS: Linux

For installing GDAL:

sudo apt-get install python3.6-dev

sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt update

sudo apt-get install gdal-bin

sudo apt-get install libgdal-dev

Dependencies:

PNG: sudo apt-get install libpng-dev

PCRE: sudo apt-get install libpcre3 libpcre3-dev

PROJ: sudo apt-get install libproj-dev

Initializing GDAL submodule:

git submodule add https://github.com/OSGeo/gdal/tree/master/gdal

For testing:

mkdir build && cd build

cmake .. -G Ninja

ninja

./raster <your_absolute_tiff_file_path>

About the project

GDAL is a translator library for raster and vector geospatial data format. The project consist in rastering a GeoTIFF file format using GDAL functionalities and sandboxed methods.

Implementation

Sandboxing...

The purpose of sandboxing is to limit the permissions 
and capabilities of library’s methods, in order to 
secure the usage of them. After obtaining the sandbox, 
the functions will be called through an Sandbox API 
(being called api in the current test) and so, the 
operations, system calls or namspaces access may be 
controlled. 

Raster process...

From gdal.h header useful methods are added to sapi 
library builded with CMake. 

One .tiff file is manipulated with GDALOpen 
functionality, which extracts a pointer to the data set
containg a list of raster bands, all pertaining to the
same area. 
Metadata, a coordinate system, a georeferencing
transform, size of raster and various other information
are kept into the data set that corresponds to the image.

To create an array containing the image information, the
dimentions needed are extracted using some specific 
GDAL(X/Y)Size functions applied to the block.
GDALRasterBand function takes care of data type conversion, one more step following: placing the
converted data (with RasterIO method) into the created
and well allocated structure.