README.md

---
tags:
- computer-vision
- semantic-segmentation
- satellite-imagery
- unet
- remote-sensing
datasets:
- CloudCover_SatelliteImagery_256x256
license: cc-by-nc-4.0
model-index:
- name: SatelliteImage_CloudSegmentation_Unet
  results:
  - task:
      name: Semantic Segmentation
      type: image-segmentation
    metrics:
    - type: iou
      value: 0.915
      name: Mean Intersection over Union (IoU)
    - type: dice_score
      value: 0.950
      name: Cloud Dice Score
---

# SatelliteImage_CloudSegmentation_Unet

## 🛰️ Overview

The **SatelliteImage_CloudSegmentation_Unet** is a **U-Net** based model designed for **semantic segmentation** of satellite imagery. Its purpose is to accurately classify every pixel in an input image as either "Cloud" or "Background/Clear Sky." This is critical for pre-processing Earth Observation (EO) data before tasks like land cover mapping or atmospheric correction. 

## 🧠 Model Architecture

The model employs the classic U-Net architecture, which is highly effective for biomedical and remote sensing segmentation due to its symmetric encoder-decoder structure with skip connections.

* **Encoder (Contracting Path):** Consists of repeated convolutional and pooling layers to capture contextual information and build high-level feature maps.
* **Decoder (Expanding Path):** Uses up-convolutional layers to increase the resolution of the feature maps.
* **Skip Connections:** Directly connect feature maps from the encoder to the corresponding layers in the decoder. This is vital for preserving fine-grained details needed for precise boundary localization.
* **Input:** RGB satellite image patches of size 256x256.
* **Output:** A 256x256 pixel-wise mask with 2 channels, representing the probability distribution for the two classes (Cloud and Background).

## 🎯 Intended Use

This model is intended for use in remote sensing and geospatial applications:

1.  **EO Data Pre-processing:** Automatically generating masks to filter out cloudy regions, ensuring the reliability of subsequent land-use classification or agricultural monitoring.
2.  **Atmospheric Science:** Quantifying cloud fraction and distribution over large geographic areas for climate modeling.
3.  **Disaster Response:** Quickly assessing the visibility of ground features (e.g., flood extent) after a weather event.

## ⚠️ Limitations

1.  **Thin/Cirrus Clouds:** The model may struggle with very thin, semi-transparent cirrus clouds, often misclassifying them as clear sky due to low contrast.
2.  **Shadows:** Cloud shadows on the ground can sometimes be mistakenly classified as cloud due to their low brightness values.
3.  **Resolution Dependence:** Trained on 256x256 patches. Applying the model directly to very high-resolution images (e.g., 4k) without appropriate tiling and handling may lead to boundary artifacts.

---

### MODEL 2: **Toxicology_StructureToxicity_GNN**

This model is a Graph Neural Network (GNN) for predicting chemical toxicity based on molecular graph structure.

#### config.json

```json
{
  "_name_or_path": "custom-graph-tox-predictor",
  "architectures": [
    "GraphConvolutionalNetwork"
  ],
  "model_type": "molecular_property_prediction",
  "graph_type": "molecular_graph",
  "node_features": 74,
  "edge_features": 12,
  "num_gcn_layers": 3,
  "hidden_dim": 128,
  "global_pooling": "readout_mean",
  "output_dim": 1,
  "task_type": "binary_classification",
  "id2label": {
    "0": "Non-Toxic",
    "1": "Toxic"
  },
  "label2id": {
    "Non-Toxic": 0,
    "Toxic": 1
  },
  "pytorch_version": "2.1.0"
}