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--- |
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tags: |
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- aerial |
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- temporal |
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- time-series |
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- construction |
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- multiview |
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- change-detection |
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- world-model |
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- urban |
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task_categories: |
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- video-classification |
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- image-classification |
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- image-segmentation |
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- object-detection |
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size_categories: |
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- small |
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license: other |
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--- |
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# CityLine — Temporal Aerial Construction Dataset (Sample) |
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**Temporal Aerial Vision · Construction Progress · Multiview Geometry · San Jose, CA** |
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CityLine is a multi-year aerial imagery sequence captured from a helicopter during the construction of a major mixed-use development in **San Jose, California**. |
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This sample highlights multiple construction phases over time, with several oblique views per capture date. |
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The full (commercial) dataset contains **hundreds of high-resolution images** with monthly coverage across several years — suitable for **world models, 3D reconstruction, change detection, construction analytics, and urban growth modeling**. |
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This dataset is a **limited preview sample** intended for evaluation and experimentation. |
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--- |
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## 📍 Project Overview |
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| Property | Value | |
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|---------|------| |
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| Project name | CityLine | |
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| Location | San Jose, California, USA | |
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| Capture type | Helicopter-based oblique aerial imagery | |
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| Resolution | 12MP JPEG (RAW available commercially) | |
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| Coverage period (full set) | 2017 → 2025 (approx.) | |
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| Temporal cadence | ~monthly | |
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| Viewpoints per capture | Multiple oblique angles | |
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| Coordinates | 37.374751, -122.032811 | |
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--- |
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## 🎯 Machine Learning Use Cases |
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| Category | Tasks Enabled | |
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|---------|---------------| |
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| **Temporal Vision** | World models, change detection, temporal consistency | |
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| **Multiview Geometry** | Structure-from-motion, NeRF, depth from motion | |
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| **Autonomy + Robotics** | Mapping, localization, spatial reasoning | |
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| **Construction Analytics** | Progress estimation, digital twins, safety monitoring | |
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| **Earth Observation** | Urban growth, infrastructure evolution | |
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--- |
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## 📁 Dataset Contents (Sample) |
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Folder structure: |
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```text |
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preview/ # resized JPEG previews for fast HF browsing |
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images/ # full-resolution JPEGs grouped by month |
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2017-12/ |
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2019-01/ |
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2020-06/ |
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2021-09/ |
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2023-06/ |
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2025-01/ |
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metadata.csv |
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``` |
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➡ Preview images are **2048px max dimension**, ideal for Hugging Face’s viewer |
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➡ Full-resolution files contain the highest-quality data for research/licensing |
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--- |
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### `metadata.csv` Schema |
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| Column | Description | |
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|--------|-------------| |
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| `project_id` | Numeric ID for the project | |
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| `project_name` | "CityLine" | |
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| `filename` | Full-resolution image filename | |
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| `preview_filename` | Lower-resolution preview filename | |
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| `date` | Capture date parsed from filename | |
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| `year_month` | Monthly grouping | |
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| `image_seq` | Sequence index derived from filename | |
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| `orbit_index` | Orbit grouping (sample = 1) | |
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| `orbit_frame` | Ordered view index (1…N) | |
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| `latitude` | Project latitude | |
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| `longitude` | Project longitude | |
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| `notes` | Optional annotation | |
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--- |
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## 🔧 Quick Usage Example |
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```python |
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import pandas as pd |
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from pathlib import Path |
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from PIL import Image |
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meta = pd.read_csv("metadata.csv") |
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# Load preview image first (fast) |
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preview_path = Path("preview") / meta['preview_filename'][0] |
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img_preview = Image.open(preview_path).convert("RGB") |
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img_preview.show() |
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# Load matching full-resolution image when needed |
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full_path = Path("images") / meta['year_month'][0] / meta['filename'][0] |
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img_full = Image.open(full_path).convert("RGB") |
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img_full.show() |
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``` |
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--- |
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## 🔐 Full Dataset Access & Licensing |
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This sample is provided for **evaluation purposes only**. |
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The complete CityLine dataset (836 images) and a library of **270+** full-lifecycle construction projects are available under commercial license: |
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- Towers |
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- Hospitals |
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- Stadiums |
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- Highways & interchanges |
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- Commercial sites |
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**Contact for full access:** |
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📧 gene@sharpshotsaerial.com |
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--- |
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## 🛰 About SharpShots Aerial |
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SharpShots Aerial specializes in long-term helicopter-based imaging of major construction and urban projects, enabling advanced mapping and AI research applications. |
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