Enhancing census surveys using satellite imagery

Raya Berova, Thomas Faria, Clément Guillo

12 March 2025

Outline

1️⃣ Introduction

2️⃣ Methodology

3️⃣ Data

4️⃣ Results

5️⃣ From experimentation to production?

6️⃣ Discussion

1️⃣ Introduction

Context

• Insee maintains a register of localized buildings (RIL)
  • Comprehensive housing data for cities with over 10k inhabitants 🏙️
  • Used to create sampling frames for census surveys 📋
• The RIL quality is good in metropolitan France ✅…
  • …but France also includes overseas territories (OTs)! 🌎
  • There, data quality is significantly lower ⚠️

Census in Overseas Territories

• A cartographic survey is conducted in OTs before each census

• Critical context, particularly in Mayotte and French Guiana:

  • Local authorities question official statistics
  • Rapid urban development
  • Difficult field conditions

• Requires significant work and is highly costly 💰

• Can we use satellite imagery to optimize census processes in OTs? 🛰️

• Satellite data as supporting evidence:
  • Independent information source 🔍
  • Continuous territorial monitoring 🔄
  • Transparent and reproducible methodology 📑

Our use cases

• Pre-cartographic survey:
  • Identify priority areas for fieldwork 🎯
  • Detect building changes since last survey 🏗️
  • Perform temporal comparisons using historical and recent imagery 📆
• Post-cartographic survey:
  • Automated change detection 🤖
  • Analysis of land-use evolution 🌳➡️🏘️
  • Assist population estimates in OTs 📊
• Extraordinary use case: Rapid response after devastating tropical cyclone “Chido”

Presentation Outline

1️⃣ Introduction

2️⃣ Methodology

3️⃣ Data

4️⃣ Results

5️⃣ From experimentation to production?

6️⃣ Discussion

2️⃣ Methodology

Semantic segmentation

Pleiades © CNES_2022, Distribution AIRBUS DS

Training a segmentation model

• Model trained for automatic segmentation from annotated examples
• Requirements:
  • Satellite image collection 🛰️
  • Production of annotations (building footprints or land cover, if available) 📍
• Model learns to reproduce annotations from images aiming to generalize on new images 🎯

From segmentation to change detection

Model used

• Model architecture 🧩:
  • Backbone: SegFormer (MiT)
  • Encoder: Transformer-based (efficient self-attention, no positional encoding) ⚙️
  • Decoder: Lightweight MLP head ✨
• Why SegFormer? 🚀:
  • No complex decoders → Efficient & scalable ⚡
  • Captures local & global context → High accuracy 🎯
  • No positional embeddings → Improved resolution generalization 📐
• Fine-tuned on our dataset 🗃️

Presentation Outline

1️⃣ Introduction

2️⃣ Methodology

3️⃣ Data

4️⃣ Results

5️⃣ From experimentation to production?

6️⃣ Discussion

3️⃣ Data

Pléiades (Very High Resolution) 🛰️

• 4 characteristics to look at:
  • Spatial resolution 📐
  • Acquisition frequency 🔄
  • Spectral resolution 🌈
  • Geographic coverage 🌍

• Characteristics:
  • 0.5m × 0.5m spatial resolution 🔍
  • 3 spectral bands (RGB) 🎨
  • Free archives, on-demand acquisition (6-8 months per department), Airbus © licensing 📅
  • Image size: 1 km² (2000 × 2000 pixels) 🖼️

Sentinel-2 (High Resolution) 🛰️

• Characteristics:
  • 10m × 10m spatial resolution 🔍
  • 13 spectral bands 🎨
  • 5-day revisit time, free access 🔄🆓
  • Image size: 6.25 km² (250 × 250 pixels) 🖼️

Reference Data (COSIA)

• Significant project by IGN colleagues 👏
• Land cover generated by AI as vector polygons for France and OTs 🗺️
• Based on IGN aerial photography at 20cm (!!) resolution
• Used as label for training data despite potential temporal misalignment

Presentation Outline

1️⃣ Introduction

2️⃣ Methodology

3️⃣ Data

4️⃣ Results

5️⃣ From experimentation to production?

6️⃣ Discussion

4️⃣ Results

• At this point we have built and trained our model, checked its performance
• Working on notebooks displaying maps… Key questions : how can we share our results ?
• Goals was not to publish an article but rather help colleagues from overseas territory => anticipate carto survey

Interactive Dashboard 📊

🌟 A picture is worth a thousand words! 🌟

👉 Access the interactive app: Click here 🚀

• Decided to build a tiny webapp … but we are datascientists be indulgent
• We computed building areas from our predictions this we get binary value for each pixel and aggregated by admin level

⚠️ Disclaimer : This is a an early stage of our application. 0 check or data quality process have been done.

• Standard leaflet map with open Street map layer show Pleiades 2023

97611 0332 : nuages in 2022

97610ﾠ0608 : different methodology of blurring for prison

97611 0237 : Our use case : detect building changes in precarious areas

• Administrative-level monitoring of building changes:
  • Selection by administrative units (municipalities, districts)
  • Quality control of detected changes
• Key features:
  • Side-by-side temporal comparison
  • Statistical indicators by area
  • Interactive visual validation

Presentation Outline

1️⃣ Introduction

2️⃣ Methodology

3️⃣ Data

4️⃣ Results

5️⃣ From experimentation to production?

6️⃣ Discussion

5️⃣ From experimentation to production?

Processing Pipeline 🛠️

1. Standardization of data acquisition—currently rudimentary. Need cloud-free, date-specific images with consistent methodology (challenge with Pléiades).
2. Training stage: cloud removal, dataset balancing, labeling, splitting.
3. PyTorch with pre-trained model from HuggingFace (requires GPU).
4. API building: prediction of images, clustering/bounding boxes, and statistics computation (building area).
5. Deployment via GeoServer and web application.
6. Results utilized by INSEE colleagues for statistical analyses.

Application Architecture 🧩

GeoServer dynamically serves satellite images.

Challenges & Perspectives

• High maintenance costs due to technical complexity 💸
• Need for specialized skills 🧑‍💻
• Complex technical environment due to:
  • Large data volumes 🗃️
  • High computational requirements ⚡
  • Reproducibility requirements ♻️
• Promising initial results supporting cartographic surveys ✅🗺️
• Potential improvements identified for each pipeline stage 🔧