{ "cells": [ { "cell_type": "markdown", "id": "8fbafe3b", "metadata": {}, "source": [ "# Introduction\n", "\n", "This notebook provides a practical **starter walkthrough** for the **GeoAI 2026 Challenge - Reaching New Heights with GeoFM Embeddings**, covering:\n", "\n", "- Dataset structure overview (embedding sources and 4-band reference labels).\n", "- Data access setup and staging instructions via EOTDL.\n", "- Quick inspection of `data/catalog.parquet` metadata.\n", "- First-look visualization for each provided input type (AlphaEarth, Tessera, TerraMind S1/S2, THOR S1/S2) and labels.\n", "- A minimal submission packaging example that creates a valid submission `.zip` with the expected folder structure.\n", "\n", "This notebook is focused on **data understanding + submission format compliance**.\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "7141a2dc", "metadata": {}, "outputs": [], "source": [ "import zipfile\n", "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "try:\n", " import tifffile # type: ignore\n", "except Exception:\n", " tifffile = None\n", "\n", "try:\n", " import rasterio # type: ignore\n", "except Exception:\n", " rasterio = None\n" ] }, { "cell_type": "markdown", "id": "352cc585", "metadata": {}, "source": [ "# Data download\n", "\n", "The challenge data is hosted on EOTDL.\n", "To get the dataset instantly, please follow this link: https://www.eotdl.com/datasets/embed2heights\n", "\n", "From there you can stage the dataset directly using the following CLI command:\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a170ef15", "metadata": {}, "outputs": [], "source": [ "# If this is your first time using the EOTDL CLI, install the package and authenticate first.\n", "!pip install eotdl\n", "!eotdl auth login " ] }, { "cell_type": "code", "execution_count": null, "id": "8c2f031c", "metadata": {}, "outputs": [], "source": [ "# Download and stage the embed2heights dataset to your local environment.\n", "!eotdl datasets get embed2heights" ] }, { "cell_type": "markdown", "id": "8b518ca9", "metadata": {}, "source": [ "If you need support on staging datasets from EOTDL, refer to: https://www.eotdl.com/docs/datasets/stage\n" ] }, { "cell_type": "markdown", "id": "978a2265", "metadata": {}, "source": [ "# Data description\n", "\n", "From the challenge brief:\n", "\n", "- **Training set**: 2024 patches, each `256 x 256` at 10 m resolution (France).\n", "- **Test set**: around 1000 patches from different regions/years.\n", "- **Inputs**: pre-computed embeddings from four Geospatial Foundation Models:\n", " - AlphaEarth (`256 x 256 x 64`)\n", " - Tessera (`256 x 256 x 128`)\n", " - TerraMind (single epoch, S1/S2)\n", " - THOR (single epoch, S1/S2)\n", "- **Labels**: 4-band TIFF per patch:\n", " - Band 1: building percentage\n", " - Band 2: vegetation percentage\n", " - Band 3: water percentage\n", " - Band 4: relative height above ground (nDSM)\n", "\n", "Evaluation combines five metrics (weighted):\n", "\n", "- `mIoU_buildings` (25%)\n", "- `mIoU_trees` (15%)\n", "- `mIoU_water` (15%)\n", "- `RMSE_building_height` (25%)\n", "- `RMSE_vegetation_height` (20%)\n", "\n", "Final leaderboard score is the weighted mean of these metrics.\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "a187feb7", "metadata": {}, "outputs": [], "source": [ "# Paths\n", "data_dir = Path(\"data\")\n", "train_dir = data_dir / \"train\"\n", "alphaearth_emb_path = train_dir / \"alphaearth_emb\"\n", "catalog_path = data_dir / \"catalog.parquet\"\n", "labels_dir = train_dir / \"labels\"\n", "terramind_s1_emb_path = train_dir / \"terramind_s1_emb\"\n", "terramind_s2_emb_path = train_dir / \"terramind_s2_emb\"\n", "tessera_emb_path = train_dir / \"tessera_emb\"\n", "thor_s1_emb_path = train_dir / \"thor_s1_emb\"\n", "thor_s2_emb_path = train_dir / \"thor_s2_emb\"" ] }, { "cell_type": "markdown", "id": "07bbdbab", "metadata": {}, "source": [ "Catalog quick inspection (metadata and sample rows)" ] }, { "cell_type": "code", "execution_count": 26, "id": "697fe2dd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "catalog.parquet loaded from: data/catalog.parquet\n", "Rows: 14,169 | Columns: 10\n", "Columns: type, stac_version, stac_extensions, datetime, id, bbox, geometry, assets, links, repository\n" ] }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "type", "rawType": "object", "type": "string" }, { "name": "stac_version", "rawType": "object", "type": "string" }, { "name": "stac_extensions", "rawType": "object", "type": "unknown" }, { "name": "datetime", "rawType": "datetime64[ns]", "type": "datetime" }, { "name": "id", "rawType": "object", "type": "string" }, { "name": "bbox", "rawType": "object", "type": "unknown" }, { "name": "geometry", "rawType": "object", "type": "unknown" }, 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