Project Info
Inspiration
Hurricane Milton, the most devastating disaster in over 30 years, left more than 3 million people without power and overwhelmed emergency services scrambling to respond. While drone technology now floods us with vast amounts of data, the real challenge lies in making sense of it in rapidly evolving environments—operators are still stuck manually sifting through critical information when time is running out. In moments of chaos, the ability to scale autonomous search-and-rescue missions and intelligently uncover patterns from data becomes essential. SkySearch enables operators to uncover hidden insights on the environment in vast seas of data by integrating real-time data on the environment, telemetry, and previous missions into a single pane of glass (software mission control system). Operators can deploy fleets of drones to investigate regions and collect video feed used to reconstruct the scene, enabling them to drill-down on areas of interest through a semantic search engine.
What it does
Our goal is to enable operators to interact with data and uncover hidden patterns effortlessly. SkySearch is built around the end-to-end search-and-rescue workflow in the following use cases: Search: Drones are deployed through the software by operators and autonomously navigate through terrain to identify objects of interest in real-time. Rescue: Operators can interact with live data to isolate hazards and locate people through a unified search interface. Based on this data, the system then recommends risk-aware, optimized rescue routes for first responders. Core features Environment reconstruction of damaged regions and infrastructure with Gaussian splatting Risk-aware pathfinding for rescue operations and pathfinding Semantic Search through disparate data sources to uncover patterns and recommend actions
How we built it
We designed an embedded architecture that enables software and hardware interfaces to bidirectionally communicate information and commands. Drone SDK used for live video streaming TP-Link Antennas for a local wifi system to create a more robust data pipeline between the drone and the software interface - rather than relying on Satellites and Wifi OpenCV and Apple Depth Pro used to process footage and classify data SingleStore for real-time database management
Challenges we ran into
Accounting for low battery drones Integration between hardware and software interfaces Balancing human judgement with autonomy
Accomplishments we're proud of
Implemented autonomous swarming framework to detect Integrated gaussian splatting Risk-aware map traversal and recommended "safe routes" for emergency responders Dynamic Data Generation to generate and query data dynamically allows for efficient testing and analysis, improving the app's responsiveness and visibility into critical information during rescue missions.
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Analysis
View
Metric
- 42
- 36
- 29
- 9
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- FastAPIIn code
- HTMLIn code
- JavaScriptIn code
- Next.jsIn code
- PythonIn code
- ReactIn code
- Tailwind CSSIn code
- TypeScriptIn code
- AWSClaimed
9 of 10 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
1.2 MB
Source files
80
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
KevinWu098/calhacks24
183 files · 295.0 MB · @ 40def8f
Structure
Interface
45 files · 25%Screens, components and styles rendered to the user.
Application logic
46 files · 25%Domain rules, services and shared utilities.
+4 more
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- JavaScript82%
- TypeScript14%
- Python3%
- CSS1%
- YAML0%
- Markdown0%
- Other (2)0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
client/package.json
npm · 37- @here/maps-api-for-javascript
- @mkkellogg/gaussian-splats-3d
- @radix-ui/react-accordion
- @radix-ui/react-checkbox
- @radix-ui/react-progress
- @radix-ui/react-separator
- @radix-ui/react-slot
- @radix-ui/react-switch
- @radix-ui/react-toast
- @react-google-maps/api
- @react-three/drei
- @react-three/fiber
- axios
- class-variance-authority
- clsx
- google-maps-react
- lucide-react
- next
- +19 more
frontend/package.json
npm · 28- @react-google-maps/api
- @types/node
- axios
- class-variance-authority
- clsx
- dotenv
- lucide-react
- react
- react-dom
- tailwind-merge
- tailwindcss-animate
- +17 more
backend/requirements.txt
pypi · 23- backend
- colorama
- email-validator
- gradio
- grpcio-status
- httptools
- importlib-metadata
- ipykernel
- itsdangerous
- jaraco.collections
- langchain-google-genai
- langgraph
- loguru
- pickleshare
- pip-chill
- pydantic-extra-types
- pydantic-settings
- pyfiglet
- +5 more
experiments/requirements.txt
pypi · 20- email-validator
- gradio
- httptools
- importlib-metadata
- ipykernel
- itsdangerous
- jaraco.collections
- loguru
- pickleshare
- pip-chill
- pydantic-extra-types
- pydantic-settings
- pyfiglet
- roboflow
- toml
- tomli
- ujson
- ultralytics
- +2 more
backend/pyproject.toml
pypi · 8- djitellopy
- fastapi
- netifaces
- python-dotenv
- singlestoredb
- uagents
- ultralytics
- uvicorn
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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