Remote Sensing Collection (code, models, papers)

.gitattributes

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+When[[:space:]]Remote[[:space:]]Sensing[[:space:]]Meets[[:space:]]Foundation[[:space:]]Model.[[:space:]]A[[:space:]]Survey[[:space:]]and[[:space:]]Beyond.pdf filter=lfs diff=lfs merge=lfs -text

When Remote Sensing Meets Foundation Model. A Survey and Beyond.pdf

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code/remote-sensing-projects-archive.zip

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models/AI-Flood-Prediction-System/.gitattributes

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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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models/AI-Flood-Prediction-System/README.md

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+---
+title: AI Flood Prediction System
+emoji: 📉
+colorFrom: indigo
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.47.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

models/AI-Flood-Prediction-System/app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import json
+import folium
+from streamlit_folium import st_folium
+import plotly.graph_objects as go
+import plotly.express as px
+from datetime import datetime, timedelta
+import requests
+
+# Page configuration
+st.set_page_config(
+    page_title="Charsadda Flood Prediction System",
+    page_icon="🌊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""<style>
+.main-header {
+    font-size: 2.5rem;
+    font-weight: bold;
+    color: #1f77b4;
+    text-align: center;
+    margin-bottom: 2rem;
+}
+.warning-box {
+    background-color: #fff3cd;
+    border-left: 5px solid #ffc107;
+    padding: 1rem;
+    margin: 1rem 0;
+}
+.danger-box {
+    background-color: #f8d7da;
+    border-left: 5px solid #dc3545;
+    padding: 1rem;
+    margin: 1rem 0;
+}
+.safe-box {
+    background-color: #d4edda;
+    border-left: 5px solid #28a745;
+    padding: 1rem;
+    margin: 1rem 0;
+}
+</style>""", unsafe_allow_html=True)
+
+# Load model and scaler
+@st.cache_resource
+def load_models():
+    try:
+        rf_model = joblib.load('random_forest_model.pkl')
+        scaler = joblib.load('scaler.pkl')
+        with open('model_metadata.json', 'r') as f:
+            metadata = json.load(f)
+        return rf_model, scaler, metadata
+    except Exception as e:
+        st.error(f"Error loading models: {e}")
+        return None, None, None
+
+rf_model, scaler, metadata = load_models()
+
+# Header
+st.markdown('<div class="main-header">🌊 AI-Powered Flood Prediction System</div>', unsafe_allow_html=True)
+st.markdown("### Charsadda, Khyber Pakhtunkhwa, Pakistan")
+
+# Sidebar
+with st.sidebar:
+    st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/3/32/Flag_of_Pakistan.svg/320px-Flag_of_Pakistan.svg.png", width=150)
+    st.title("Navigation")
+    page = st.radio("Select Page", ["🏠 Home", "🔮 Flood Prediction", "🗺️ Interactive Map", "📊 Model Info", "ℹ️ About"])
+    st.markdown("---")
+    st.markdown("### Study Area")
+    st.markdown("**Location:** Charsadda District")
+    st.markdown("**Province:** Khyber Pakhtunkhwa")
+    st.markdown("**Coordinates:** 34.15°N, 71.72°E")
+
+# HOME PAGE
+if page == "🏠 Home":
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown("### 📋 Project Overview")
+        st.write("""This AI-powered system predicts flood risks in Charsadda district using:
+        - **Satellite imagery** (Sentinel-1 SAR & Sentinel-2 optical)
+        - **Weather data** (temperature, humidity, rainfall)
+        - **Topographic data** (elevation, slope)
+        - **Machine Learning** (Random Forest, CNN, LSTM models)""")
+        st.markdown("### 🎯 Key Features")
+        st.markdown("""- ✅ Real-time flood risk assessment
+        - ✅ Multi-source data integration
+        - ✅ Interactive flood mapping
+        - ✅ 1-3 day advance predictions
+        - ✅ High-accuracy ML models""")
+    with col2:
+        st.markdown("### 📈 Historical Floods")
+        st.info("""**2022 Flood (August 25-31)**\n- Affected thousands of people\n- Major infrastructure damage\n- Rivers Swat and Kabul overflowed\n\n**2025 Floods (June & August)**\n- Multiple flood events\n- ~35 km² area affected\n- ~41,000 people impacted""")
+        st.markdown("### 🚨 Current Status")
+        st.success("System operational and monitoring ongoing")
+
+# FLOOD PREDICTION PAGE
+elif page == "🔮 Flood Prediction":
+    st.markdown("## Flood Risk Prediction")
+    tab1, tab2 = st.tabs(["📤 Upload Data", "⌨️ Manual Input"])
+    
+    with tab1:
+        st.markdown("### Upload Weather Forecast CSV")
+        st.info("Upload a CSV file with columns: temperature, humidity, rainfall, pressure, wind_speed")
+        uploaded_file = st.file_uploader("Choose a CSV file", type=['csv'])
+        
+        if uploaded_file is not None:
+            df = pd.read_csv(uploaded_file)
+            st.write("**Preview of uploaded data:**")
+            st.dataframe(df.head())
+            
+            if st.button("Predict Flood Risk", key="predict_upload"):
+                df['VV'] = -10
+                df['B2'] = 800
+                df['B3'] = 900
+                df['B4'] = 600
+                df['B8'] = 2000
+                df['B11'] = 1500
+                df['NDWI'] = 0.0
+                df['MNDWI'] = 0.0
+                df['NDVI'] = 0.4
+                df['elevation'] = 300
+                df['slope'] = 2
+                
+                feature_order = metadata['features']
+                df = df[feature_order]
+                X_scaled = scaler.transform(df)
+                predictions = rf_model.predict(X_scaled)
+                probabilities = rf_model.predict_proba(X_scaled)[:, 1]
+                
+                df['Flood_Risk'] = predictions
+                df['Confidence'] = probabilities * 100
+                
+                st.markdown("### Prediction Results")
+                flood_count = predictions.sum()
+                col1, col2, col3 = st.columns(3)
+                col1.metric("Total Predictions", len(predictions))
+                col2.metric("Flood Risk Days", int(flood_count))
+                col3.metric("Risk Percentage", f"{flood_count/len(predictions)*100:.1f}%")
+                st.dataframe(df[['temperature', 'humidity', 'rainfall', 'Flood_Risk', 'Confidence']])
+    
+    with tab2:
+        st.markdown("### Manual Weather Input")
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            temperature = st.slider("Temperature (°C)", 15.0, 45.0, 30.0, 0.5)
+            humidity = st.slider("Humidity (%)", 30.0, 100.0, 70.0, 1.0)
+            rainfall = st.slider("Rainfall (mm)", 0.0, 300.0, 50.0, 5.0)
+        with col2:
+            pressure = st.slider("Pressure (hPa)", 950.0, 1050.0, 1010.0, 1.0)
+            wind_speed = st.slider("Wind Speed (m/s)", 0.0, 30.0, 5.0, 0.5)
+        
+        if st.button("Predict Flood Risk", key="predict_manual"):
+            input_data = pd.DataFrame([{
+                'VV': -10, 'B2': 800, 'B3': 900, 'B4': 600, 'B8': 2000, 'B11': 1500,
+                'NDWI': 0.0, 'MNDWI': 0.0, 'NDVI': 0.4, 'elevation': 300, 'slope': 2,
+                'temperature': temperature, 'humidity': humidity, 'rainfall': rainfall,
+                'pressure': pressure, 'wind_speed': wind_speed
+            }])
+            
+            input_data = input_data[metadata['features']]
+            X_scaled = scaler.transform(input_data)
+            prediction = rf_model.predict(X_scaled)[0]
+            probability = rf_model.predict_proba(X_scaled)[0, 1] * 100
+            
+            st.markdown("---")
+            st.markdown("### Prediction Result")
+            
+            if prediction == 1:
+                if probability > 75:
+                    st.markdown(f'<div class="danger-box"><h3>🚨 HIGH FLOOD RISK</h3><p>Confidence: {probability:.1f}%</p><p><strong>Action Required:</strong> Immediate evacuation recommended.</p></div>', unsafe_allow_html=True)
+                else:
+                    st.markdown(f'<div class="warning-box"><h3>⚠️ MODERATE FLOOD RISK</h3><p>Confidence: {probability:.1f}%</p><p><strong>Action Required:</strong> Stay alert.</p></div>', unsafe_allow_html=True)
+            else:
+                st.markdown(f'<div class="safe-box"><h3>✅ LOW FLOOD RISK</h3><p>Confidence: {100-probability:.1f}%</p><p>Conditions are favorable.</p></div>', unsafe_allow_html=True)
+            
+            fig = go.Figure(go.Indicator(
+                mode="gauge+number", value=probability, title={'text': "Flood Risk Level"},
+                gauge={'axis': {'range': [0, 100]}, 'bar': {'color': "darkblue"},
+                       'steps': [{'range': [0, 33], 'color': "lightgreen"},
+                                 {'range': [33, 66], 'color': "yellow"},
+                                 {'range': [66, 100], 'color': "red"}],
+                       'threshold': {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 50}}
+            ))
+            fig.update_layout(height=300)
+            st.plotly_chart(fig, use_container_width=True)
+
+# INTERACTIVE MAP PAGE
+elif page == "🗺️ Interactive Map":
+    st.markdown("## Interactive Flood Risk Map")
+    st.info("This map shows the Charsadda study area with historical flood zones.")
+    
+    m = folium.Map(location=[34.15, 71.72], zoom_start=11)
+    boundary_coords = [[34.05, 71.55], [34.05, 71.90], [34.25, 71.90], [34.25, 71.55], [34.05, 71.55]]
+    folium.Polygon(locations=boundary_coords, color='red', weight=3, fill=False, popup='Charsadda Study Area').add_to(m)
+    folium.Marker([34.15, 71.72], popup='Charsadda Center', tooltip='Charsadda District', icon=folium.Icon(color='blue', icon='info-sign')).add_to(m)
+    
+    flood_zones = [([34.10, 71.65], '2022 Flood Zone', 'red'),
+                   ([34.18, 71.75], '2025 June Flood', 'orange'),
+                   ([34.12, 71.80], '2025 August Flood', 'darkred')]
+    for coords, name, color in flood_zones:
+        folium.Circle(location=coords, radius=2000, popup=name, color=color, fill=True, fillColor=color, fillOpacity=0.4).add_to(m)
+    
+    st_folium(m, width=1000, height=600)
+    st.markdown("### Map Legend")
+    col1, col2, col3 = st.columns(3)
+    col1.markdown("🔴 **2022 Flood Zone**")
+    col2.markdown("🟠 **2025 June Flood**")
+    col3.markdown("🔴 **2025 August Flood**")
+
+# MODEL INFO PAGE
+elif page == "📊 Model Info":
+    st.markdown("## Model Information")
+    if metadata:
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown("### Model Details")
+            st.info(f"""**Best Model:** {metadata['best_model']}\n**Training Date:** {metadata['training_date'][:10]}\n**Features:** {metadata['n_features']}\n**Training Samples:** {metadata['training_samples']:,}\n**Test Samples:** {metadata['test_samples']:,}""")
+        with col2:
+            st.markdown("### Performance Metrics")
+            metrics_df = pd.DataFrame(metadata['performance_metrics'])
+            st.dataframe(metrics_df, use_container_width=True)
+        st.markdown("### Features Used")
+        st.write(metadata['features'])
+        fig = px.bar(metrics_df, x='Model', y=['Accuracy', 'Precision', 'Recall', 'F1-Score', 'ROC-AUC'], barmode='group', title='Model Performance Comparison')
+        st.plotly_chart(fig, use_container_width=True)
+
+# ABOUT PAGE
+elif page == "ℹ️ About":
+    st.markdown("## About This Project")
+    st.markdown("""### AI-Powered Flood Prediction and Emergency Response System
+    
+#### Objective
+To develop an AI-based flood prediction system that provides early warnings and supports efficient coordination of emergency services for Charsadda district, KPK, Pakistan.
+
+#### Methodology
+- **Multi-Source Data Fusion:** Combines Sentinel-1 SAR, Sentinel-2 optical imagery, SRTM elevation data, and weather forecasts
+- **Machine Learning Models:** Random Forest, CNN, and LSTM models trained on historical flood events (2022 and 2025)
+- **Real-time Monitoring:** Integration with OpenWeatherMap API for current conditions
+- **Prediction Timeframe:** 1-3 days advance flood risk assessment
+
+#### Data Sources
+- **Satellite:** Sentinel-1, Sentinel-2 via Google Earth Engine
+- **Elevation:** SRTM Digital Elevation Model
+- **Weather:** OpenWeatherMap API
+- **Historical Events:** 2022 and 2025 flood data
+
+#### Technical Stack
+- **Backend:** Python, scikit-learn, TensorFlow, Google Earth Engine
+- **Frontend:** Streamlit
+- **Deployment:** Hugging Face Spaces
+- **Geospatial:** Folium, Geopandas, GDAL
+
+#### Impact
+- Early warning system for flood-prone communities
+- Optimized emergency resource allocation
+- Reduced flood-related casualties and economic losses
+- Enhanced disaster preparedness and response""")
+    st.markdown("---")
+    st.markdown("### 📧 Contact & Support")
+    st.info("For questions or support, please contact the development team.")
+
+# Footer
+st.markdown("---")
+st.markdown('<div style="text-align: center; color: gray;"><p>© 2025 Charsadda Flood Prediction System | Powered by AI & Satellite Data</p></div>', unsafe_allow_html=True)

models/AI-Flood-Prediction-System/model_metadata.json

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+{
+  "best_model": "Random Forest",
+  "training_date": "2025-10-28T03:42:55.678155",
+  "performance_metrics": [
+    {
+      "Model": "Random Forest",
+      "Accuracy": 1.0,
+      "Precision": 1.0,
+      "Recall": 1.0,
+      "F1-Score": 1.0,
+      "ROC-AUC": 1.0
+    },
+    {
+      "Model": "CNN",
+      "Accuracy": 1.0,
+      "Precision": 1.0,
+      "Recall": 1.0,
+      "F1-Score": 1.0,
+      "ROC-AUC": 1.0
+    },
+    {
+      "Model": "LSTM",
+      "Accuracy": 0.5003340013360054,
+      "Precision": 0.5005847953216375,
+      "Recall": 0.5714285714285714,
+      "F1-Score": 0.5336658354114713,
+      "ROC-AUC": 0.5032726344573514
+    }
+  ],
+  "features": [
+    "VV",
+    "B2",
+    "B3",
+    "B4",
+    "B8",
+    "B11",
+    "NDWI",
+    "MNDWI",
+    "NDVI",
+    "elevation",
+    "slope",
+    "temperature",
+    "humidity",
+    "rainfall",
+    "pressure",
+    "wind_speed"
+  ],
+  "n_features": 16,
+  "training_samples": 7004,
+  "test_samples": 1500
+}

models/AI-Flood-Prediction-System/random_forest_model.pkl

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+size 134313

models/AI-Flood-Prediction-System/requirements.txt

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+streamlit==1.31.0
+pandas==2.1.4
+numpy==1.26.3
+scikit-learn==1.3.2
+joblib==1.3.2
+folium==0.15.1
+streamlit-folium==0.15.1
+plotly==5.18.0
+requests==2.31.0

models/AI-Flood-Prediction-System/sample_forecast.csv

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+temperature,humidity,rainfall,pressure,wind_speed
+28,75,20,1010,5
+30,80,50,1008,8
+32,85,100,1005,12
+31,82,80,1007,10
+29,78,30,1012,6

models/AI-Flood-Prediction-System/scaler.pkl

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models/AI-Flood-Prediction-System/source.txt

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+https://huggingface.co/spaces/Sumayyea/AI-Flood-Prediction-System

models/abandoned_home_detector_mini_v1/.gitattributes

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models/abandoned_home_detector_mini_v1/README.md

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+---
+pipeline_tag: object-detection
+license: apache-2.0
+language:
+- en
+---
+
+# Abandoned Home Detector
+
+This model is designed to detect abandoned homes from images by identifying high-confidence indicators, such as:
+- Damaged roofs
+- Surface damage
+- Boarded-up windows
+- Graffiti
+
+The model is built using YOLOv8, optimized for detecting these indicators with polygon-based annotations, and supports PyTorch framework for deployment. It can assist in tasks such as urban area analysis, property risk assessment, and disaster recovery planning.

models/abandoned_home_detector_mini_v1/abandoned_house_detector_mini_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:79e1de35ddb86b52fb04af01f98d2c52abe71e08daff699674f0bd9203448629
+size 22520803

models/abandoned_home_detector_mini_v1/config.json

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+{
+  "framework": "PyTorch",
+  "name": "Abandoned Home Detector Mini v1",
+  "type": "detector",
+  "description": "This model detects abandoned homes by identifying high-confidence indicators such as damaged roofs, surface damage, boarded-up windows, and graffiti. Designed with YOLOv8 for polygon-based annotation.",
+  "labels": [
+    {
+      "id": 1,
+      "name": "damaged_roof",
+      "attributes": [],
+      "color": "#157177"
+    },
+    {
+      "id": 2,
+      "name": "surface_damage",
+      "attributes": [],
+      "color": "#31fa0d"
+    },
+    {
+      "id": 3,
+      "name": "boarded_up_window",
+      "attributes": [],
+      "color": "#4cb5c7"
+    },
+    {
+      "id": 4,
+      "name": "graffiti",
+      "attributes": [],
+      "color": "#f59331"
+    }
+  ],
+  "mapping": {
+    "damaged_roof": "damaged_roof",
+    "surface_damage": "surface_damage",
+    "boarded_up_window": "boarded_up_window",
+    "graffiti": "graffiti"
+  }
+}

models/abandoned_home_detector_mini_v1/source.txt

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+https://huggingface.co/prestonzen/abandoned_home_detector_mini_v1

models/forest_plot_analysis_2024_2025/.gitattributes

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models/forest_plot_analysis_2024_2025/README.md

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+---
+license: cc-by-nc-sa-4.0
+---
+
+
+## Description
+
+Set of weights generated for the project of forest plot analysis for the course of 
+Remote Sensing in Grenoble-INP ENSE3 2024/2025.
+
+The related project is available at:
+
+https://gricad-gitlab.univ-grenoble-alpes.fr/piconed/remote-sensing-projects-archive
+
+in the folder `projects/2024_2025/05_forest`
+
+
+## Credits
+
+- Julien Delahaye (Julien.Delahaye1@grenoble-inp.org)
+- Nicolas Dessailly (Nicolas.Desailly@grenoble-inp.org)
+- Raphaël Evrard (Raphael.Evrard@grenoble-inp.org)
+- Carla Philippon (Carla.Philippon@grenoble-inp.org)

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models/land_use_classifier/DESCRIPTION.txt

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+Обычный сверточный классификатор земельного покрова (land-use / land-cover). Небольшой ResNet-18 классификатор землепользования, специально уменьшенный под маленькую задачу.
+По архитектуре упрощённый ResNet-подобный CNN: несколько стеков Conv -> ReLU -> Add с остаточными связями, затем GlobalAveragePool и финальный Gemm на 21 класс.
+Применение: только baseline-классификация 21 класса land-use.
+
+### УСТРОЙСТВО МОДЕЛИ ###
+
+Параметров:   ~11.1 млн
+Вход:         1x3x256x256
+Выход:        1x21
+
+### ЧТО ДЕЛАЕТ МОДЕЛЬ ###
+
+По входному RGB-изображению 256x256 выдает вероятность 21 класса землепользования.
+
+Примеры типов классов:
+ > сельхозземли
+ > лес
+ > вода
+ > жилые районы
+ > промзоны
+ > дороги
+ > пустыни
+ > горные массивы
+ > и т.д.
+(конкретные 21 класса зависят от набора данных, обычно UC Merced Land Use или EuroSAT)
+
+Никаких функций обнаружения объектов, сегментации, change-detection, геопространственного reasoning нет.
+Модель не геопривязана. Она не знает, где север, где страна, какая проекция. Это обычная картинка.
+
+Классы фиксированные (21), модель не умеет понимать новые классы.
+Не работает с тайлами разных масштабов. Только 256x256.
+
+Низкая точность на реальных спутниковых данных. Модель обучена на датасете с высоким качеством и без шумов.
+Реальное разрешение (Sentinel-2, 10-20 m/px) она классифицирует плохо.

models/land_use_classifier/README.md

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models/landuse_ratio_classification_geochat/source.txt

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models/landuse_ratio_classification_geochat/special_tokens_map.json

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+size 499723

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models/remote-sensing-Qwen2-VL-2B-Instruct-GGUF/source.txt

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models/ship_detection_homemade_2024_2025/.gitattributes

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models/ship_detection_homemade_2024_2025/README.md

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+---
+license: cc-by-nc-sa-4.0
+---
+
+## Description
+
+Weights generated for a homemade model of the project of
+ship detection for Remote Sensing in Grenoble-INP ENSE3 2024/2025.
+
+The related project is available at:
+
+https://gricad-gitlab.univ-grenoble-alpes.fr/piconed/remote-sensing-projects-archive
+
+in the folder `projects/2024_2025/08_ship_detection`
+
+
+## Credits
+
+Hugo Fournier (Hugo.Fournier2@grenoble-inp.org)  
+Lilian Haemmerer (Lilian.Haemmerer@grenoble-inp.org)  
+Paul Jablonski (Paul.Jablonski@grenoble-inp.org)

models/ship_detection_homemade_2024_2025/model_weights.pth

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models/ship_detection_homemade_2024_2025/source.txt

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+https://huggingface.co/remote-sensing-ense3-grenoble-inp/ship_detection_homemade_2024_2025

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models/ship_detection_yolo8_2024_2025/README.md

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+---
+license: cc-by-nc-sa-4.0
+---
+
+## Description
+
+Collection of weights for the yolo8 model for the project of
+ship detection for Remote Sensing in Grenoble-INP ENSE3 2024/2025.
+
+The related project is available at:
+
+https://gricad-gitlab.univ-grenoble-alpes.fr/piconed/remote-sensing-projects-archive
+
+in the folder `projects/2024_2025/08_ship_detection`
+
+The related dataset used for training is available at:
+https://huggingface.co/datasets/remote-sensing-ense3-grenoble-inp/ship_detection_roboflow_2024_2025
+
+
+## Credits
+
+Hugo Fournier (Hugo.Fournier2@grenoble-inp.org)  
+Lilian Haemmerer (Lilian.Haemmerer@grenoble-inp.org)  
+Paul Jablonski (Paul.Jablonski@grenoble-inp.org)

models/ship_detection_yolo8_2024_2025/checkpoint_best_total.pth

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models/ship_detection_yolo8_2024_2025/source.txt

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+https://huggingface.co/remote-sensing-ense3-grenoble-inp/ship_detection_yolo8_2024_2025

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models/urban_classification_2024_2025/README.md

@@ -0,0 +1,23 @@
+---
+license: cc-by-nc-sa-4.0
+---
+
+
+## Description
+
+Set of weights generated for urban classification for the project of Remote 
+Sensing in Grenoble-INP ENSE3 2024/2025.
+
+The related project is available at:
+
+https://gricad-gitlab.univ-grenoble-alpes.fr/piconed/remote-sensing-projects-archive
+
+in the folder `projects/2024_2025/03_urban`
+
+
+## Credits
+
+Celian Charrin (Celian.Charrin@grenoble-inp.org)
+Alexandre Jolly (Alexandre.Jolly@grenoble-inp.org)
+Morgan Santalucia (Morgan.Santalucia@grenoble-inp.org)
+Timon Taule--Bonnaire (Timon.Taule--Bonnaire@grenoble-inp.org)