Hugo Roussaffa a remote geomatic expert

Annotation AEP Helper

Wed, 01 Jan 2025 00:00:00 +0000

Project description

The Annotation AEP Helper plugin was developed in the context of the GIS DAY 2025 presentation. It aims to facilitate the integration of recollements (geometric annotations) into the GEOcalédonian standard for Earth observation and territorial planning projects.

Objectives

Automate the creation of recollements conforming to the GEOcalédonian standard
Reduce data preparation time for Earth observation projects
Ensure annotation compatibility with local GIS systems
Promote reusability of annotation data between projects

Key features

Import of existing annotation files
Automatic conversion to GEOcalédonian recollement formats
Geographical validation against local standards
Export to common formats (Shapefile, GeoJSON, GeoPackage)
Intuitive interface within the QGIS environment

Technologies used

QGIS: interface and geometry processing
Python: plugin development and georeferencing logic
GEOcalédonian standard: specific formats and constraints
AEP: Earth observation activities

Image gallery

Installation

# Via GitHub repository
git clone https://github.com/yougis/annotation_AEP_helper

# Or manual installation
# Detailed instructions in the repository README

Useful links

Source repository: https://github.com/yougis/annotation_AEP_helper
GIS DAY 2025 presentation: available in shared workspace
Technical documentation: README included in the repository

Public presentations of geomatics projects

Thu, 23 Feb 2023 00:00:00 +0000

Public talks presenting geomatics projects and open data initiatives:

Geomatics club — quarterly technical meeting, 2023
Cluster OpenNC — Open Data, a winning combination

Presentation slides

Download the slides (PDF)

ESRI Dashboard / Light pollution

Sun, 01 Jan 2023 00:00:00 +0000

Description du projet

This project aims to develop an interactive dashboard using the ESRI platform to visualize and analyze environmental interest zones under pressure (gestion de l’eau , erosion, light pollution ..). Theses dashboards will integrate many detailed spatial data from plenty of data sources.

Compétences exploitées et livrables

ArcGIS Online ArcGIS Data Visualisation Data Management

Galerie d’images

ESRI Dashboard / Water management

Sat, 01 Jan 2022 00:00:00 +0000

📝 Drinking water resource monitoring project in New Caledonia

Context: As part of my role within an environmental association, I was responsible for the information systems and geomatics projects division. I was given a simple mission: to design a territorial dashboard for monitoring the drinking water resource across New Caledonia, for a public institution (DAVAR).

Objectives:

Identify, formalize and produce around ten relevant geographic indicators for monitoring the resource.
Build a sustainable and reproducible technical solution, scalable in the long term.
Automate the processing and aggregation of vector geographic data (provided by the contracting authority).

Role and actions:

Design and development of a scripted geospatial processing framework (Python/GDAL, YAML), with geometric intersection, filtering, aggregation steps, etc.
Structuring data according to a bronze / silver / gold logic (Data Lakehouse model adapted to geographic data).
Setting up a data catalog and standardized models to facilitate multi-scale analysis.
Explicit refusal of manual processing (QGIS-type), in favor of an industrialized, reproducible and documented system.
Constant dialogue with public partners to refine indicators and anticipate future uses, applying an agile approach.

Results and impacts:

Successful delivery of the first version of the dashboard and indicators.
Adoption of the framework as the technical foundation for all future environmental projects of the observatory.
Significant time savings in subsequent projects and greater reliability of processing.
Building a culture of automated and maintainable processing within the team.

Lessons learned:

The importance of imposing a clear and robust technical vision, even when it diverges from existing practices.
Training and pedagogy are key factors in getting teams to adopt more advanced technical tools.
A well-considered methodological choice can generate a lasting leverage effect at the scale of an organization.

Skills used

Data Visualisation

Data Management

Python Development

Data Automation

Data Lakehouse

Data Processing

Data Analysis

Data Engineering

Technologies used

Python

GDAL

YAML

DBT

ArcGIS Online

ArcGIS

Bibliothèques Python

Pandas
Library for data manipulation and analysis.
rasterio
Reading and writing geospatial raster data.
geopandas
Pandas extension for working with geospatial data.
intake
Data discovery, management and access.
Dask
Parallel processing of large-scale data.

Indexing all environmental information on an H3 vector grid

Sat, 01 Jan 2022 00:00:00 +0000

Project description

The goal is to build a decision support system based on geographic and environmental data: wildfires, land cover, administrative boundaries, drought indicators, light pollution, and more. The data is indexed on an H3 grid for fine-grained spatial analysis. Processing is carried out with Python algorithms and the datamarts are generated with DBT, a SQL generator based on Jinja templates.

Our project stands out from standard business intelligence projects through its ability to integrate geographic and environmental data, to perform fine-grained spatial analysis thanks to H3 indexing, and to stay flexible enough to meet specific needs.

H3 is a hexagonal spatial indexing system developed by Uber that divides the Earth’s surface into a grid of hexagons of variable sizes. It is particularly useful for fine-grained spatial analysis of data, as it allows data to be stored at different levels of granularity.

The advantages of using H3 over a standard geographic information system are numerous:

It adapts the fineness of the spatial analysis, since data can be stored at different levels of spatial granularity.
It is also more efficient in terms of data storage, as it can store data of different sizes in a single grid.
It works easily with visualization tools such as geographic dashboards, giving users a better understanding of the data.

Using H3, we can better understand the risks related to wildfires, drought and light pollution. H3 indexing enables fine-grained spatial analysis, meaning we can identify the areas most affected by drought or light pollution with greater accuracy.

DBT is a key tool for our project because it lets us generate datamarts from our data. It uses Jinja templates to generate SQL queries that can be used to build geographic dashboards and automated reports. This gives users a better understanding of the data and enables more effective decision-making.

In short, our project is based on geographic and environmental data, fine-grained spatial analysis through H3 indexing, and flexibility to meet specific needs, using DBT to generate datamarts, geographic dashboards and automated reports.

Skills used and deliverables

Python
Data Visualisation
Data Management

Large-scale environmental geoprocessing and spatial analysis

Sat, 01 Jan 2022 00:00:00 +0000

What does Bilbo mean?

Bilbo stands for Business Intelligence for Land and Biodiversity Observation. It is a research and development project aimed at developing indicators for Earth observation.

Project objective

The environmental observatory’s goal is to translate the state, pressures and responses affecting the environment in New Caledonia. To meet these objectives, we collect and analyze environmental monitoring data produced locally by local authorities, mining companies, research institutes, associations and businesses, as well as data produced worldwide, such as that obtained from Earth observation satellites:

land cover and erosion,
wildfires,
vegetation drought indicators,
the light pollution indicator
…

The amount of information to be analyzed is massive and constantly growing — this is Big Data. To optimize our productivity in monitoring, we aim to automate processing and the publication of information in a decision-ready format.

Main challenges

Heavy, time-consuming geoprocessing

Spatial relationships

In a classic (non-spatial) BI project, the added value comes from crossing data in a model structured by relationships between information: reference keys. In our case, the relationship between information is ALSO spatial, and this specificity implies heavier calculations than key-based relationships.

From GIS to a decision support system

The approach we have followed so far consists of transposing the model of a Geographic Information System into a Decision Support System, which means defining which facts and which dimensions will allow us to answer all our questions.

The facts

Our first results led to the production of data made up of the results of geographic intersections between environmental pressures (burned areas, for example), context information (land cover types) and administrative perimeter information (municipal boundaries). The result of these divisions constitutes our DataWareHouse, which provides aggregated results according to our temporal, thematic and above all spatial dimensions.

This model is not fully satisfactory because it multiplies the crossings performed for each of the themes analyzed. Databases are weighed down by redundant information, notably due to the inclusive nature of the data depending on the scales studied (a region encompasses +/- several municipalities). Moreover, the heterogeneity of the precision of the data we cross sometimes leads to a patchwork of objects producing geometric invalidities or semantic inconsistencies.

The DataMarts (DTM)

These are the tables resulting from the aggregation of facts according to the chosen dimensions. These tables are then used to produce reports, maps and statistical analyses. They are produced by SQL scripts and stored in a relational database. The SQL scripts are redundant, so the use of the DBT framework brings an improvement in terms of maintainability and readability of the scripts.

Towards a more efficient approach

The side effects of our current approach led me to think about a more efficient, more robust and more flexible approach: the use of a lightweight spatial index that optimizes spatial relationship calculations. The following article presents this approach:

Indexing all environmental information on an H3 vector grid

QGIS Atlas / Wildfires

Sat, 01 Jan 2022 00:00:00 +0000

Project description

Building a complex cartographic atlas with QGIS, retracing the history of wildfires in every municipality of New Caledonia over the past 20 years. The atlas combines land cover data, administrative boundaries and fire histories to produce a series of consistent, reproducible map sheets.

The production chain relies on the QGIS atlas generator: a single layout model, parameterized by municipality, automatically generates all the map sheets. Each sheet includes the maps, municipal statistics and legend elements needed to read wildfire dynamics.

Skills used and deliverables

QGIS
Data Visualisation
Data Management

Image gallery

Vegetation drought monitoring by remote sensing

Sat, 01 Jan 2022 00:00:00 +0000

This project follows on from the work carried out under the Space Climate Observatory project, named EO4DroughtMonitoring1 in New Caledonia, with the main objective of demonstrating the applicability and usefulness of such a tool in other Pacific island territories. The overall approach is to have a process adapted to all Pacific island territories that could subsequently be delivered as an open-access operational service in partnership with the region’s key stakeholders.

In the context of this project, we carried out:

the construction of a vegetation drought indicator capable of characterizing the current situation as well as historical and forecast situations, depending on the data available from the various satellite platforms and meteorological instruments of the territories concerned,
the production deployment of the indicator in New Caledonia, with the aim of demonstrating the operational nature of the solution,
the adaptation of the production chain for certain territories in the Pacific region,
the promotion of the data through interfaces and consultation and distribution services.

View the full interactive report by clicking this link

ESRI Dashboard / Wildfires

Fri, 01 Jan 2021 00:00:00 +0000

Description du projet

This project aims to develop an interactive dashboard using the ESRI platform to visualize and analyze environmental interest zones under pressure (wildfire , erosion, light pollution ..). Theses dashboards will integrate many detailed spatial data from plenty of data sources.

Compétences exploitées et livrables

ArcGIS Online ArcGIS Data Visualisation Data Management

Galerie d’images

Atlas du Vélib

Fri, 01 Jan 2010 00:00:00 +0000

Projet mené à quatre mains avec Jules Grandin.

Analyse spatiale, simulation de parcours, Classification acendantes Hierarchiques, statistiques multivariées, réalisation de la maquette, des cartes et des textes.

Galerie d’images

Mon, 01 Jan 0001 00:00:00 +0000