@HackerDpro

I have been a bit quiet lately, here is what is happening:

I am on holidays with my family in Spain, this is what is keeping my time limited. I also don’t have a desktop here so that makes working and coding a bit more difficult (I work on a 14” schoollaptop).

I am working on a very big update, about adding satelite cams and nearby webcams to visualize fires and maybe other things. This is a bit harder than expected but I expect it to be done in some hours, this would be the special update for celebrating my 25 or 30 hours in total coding time.

I am also trying to make the feedback recieved from the shipping true, removing some bugs users found, …

If you are reading this maybe you would like to try it out, I really appreciate feedback because it helps me improve what I don’t see.
https://climate-shield.netlify.app/

Changing to a real server & a new TACTICAL UI

Added a new Dropdown menu to the TACTICAL page to filter threats by layers and fully changed the backend to the Hack Club Nest server!

The biggest friction this week was dealing with my old Render backend constantly going to sleep, which made the web app feel broken to new users. I finally set up my FastAPI app with PM2 so it stays awake 24/7.

On the frontend, I built a sleek new dropdown to change between Earthquakes, Floods, Storms, and Volcanoes. The hard part was getting the map layers to play nicely together, toggling the new NASA API’s kept overwriting my core 6,000+ wildfire array! I had to rewrite the logic to separate the Leaflet layers so the map can stays fast and doesn’t wipe your data when you switch views.

Right now, I am still trying to fix some bugs and improving the CO2 graph, I am also planning on updating my README.md to explain the new features (better).

THANK YOU FOR READING!
(I am almost at 20 hours of coding omg, I may need to add a special feature for when it happens. Any ideas or improvements are really appreciated!)

I hit 10 hours of coding!

Here is what i lately changed:
I just made a clock in the Tactical page (who doesn’t need a clock).

I changed the font-family to Roboto.

And I also changed the colors and some styling to feel more personal (I got much comments saying that I used AI so I am currently rewriting the most of the css to feel more like me).

Lastly I’ve remade the whole (first section) of the README.md, no more AI is used in it.

I have made some improvement on the Orbital to be able to display the extra sensors too (btw extra sensors is a new feature too, read the README.md to learn more about it).

I also made a costum favicon.

Right now I am gathering API’s for more data and info comming soon, and also trying to categorize the things better so my site doesn’t look too messy (to new users).

Here is a summary of what I added on Day 3:

Orbital Engine: Implemented a 3D WebGL globe with hardware-accelerated solar shading and physical height-mapped thermal pillars.

Predictive “Pre-Crime” Scanning: Created a sector-based scan that analyzes soil moisture and weather data to calculate ignition probability for any empty landmass.

Tactical Evacuation Routing: Integrated the Open Source Routing Machine (OSRM) to automatically generate safe civilian escape paths based on wind direction and terrain.

Multi-Hazard Sensor Array: Added specialized monitoring layers for Mudslides (soil hydrophobicity), Cyclones (SST anomalies), Volcanic Ash (stratospheric tracking), and Seismic Activity (USGS lithospheric data).

Raw Radiometry Engine: Added the ability to ingest NASA MODAPS CSV data to measure physical fire intensity in Megawatts (FRP), rather than just visual location.

Performance Engineering: Optimized massive dataset rendering by offloading complex physics calculations to background CPU Web Workers to ensure zero UI lag.

More info in https://github.com/HackerDpro/climate-shield

Read the README.dm for a full resume but here is a shortened version of my day 2:

I’ve hit a major milestone in the development of Climate Shield.
Scaling a dashboard to handle 6,000+ live wildfire events while maintaining a responsive user interface required a total re-architecture of my data pipeline.

Technical Challenges & Solutions:

Performance Scaling:
To prevent browser crashes, I migrated from standard map markers to an HTML5 Canvas rendering engine. This allows for thousands of data points to load near-instantly without lag.

API Intelligence:
To handle 6,000+ potential weather requests without triggering rate limits, I implemented an “on-demand” fetching model. Wind vectors and elevation-based terrain analytics now trigger only when a user interacts with a specific fire, ensuring the dashboard remains fast and efficient.

Terrain-Aware Analytics:
I integrated elevation data to model fire spread. The engine now calculates whether a fire is on a “Critical Uphill Alignment”—a major factor in real-world wildfire behavior—providing a 24-hour linear projection for the most dangerous incidents.

Dashboard Evolution:
I’ve evolved the UI into a true Command Center with three distinct view modes:Tactical: The default full-dashboard view.Orbital: A map-focused mode for spatial tracking.

Telemetry:
A high-level data engine featuring carbon flux calculations, predictive risk averages, and hazard distribution matrices.

This transition from a simple map to a multi-layered predictive engine has significantly improved the tool’s utility. Whether it’s tracking global CO2 trajectories or specific uphill fire spread, Climate Shield is now a high-performance asset for wildfire visualization.

Spent our first session setting up the entire architecture for Climate Shield.
Instead of just building a static map, we built a live data bridge.
We successfully connected a Python backend engine using FastAPI to NASA’s live EONET satellite network, pulling in over 6,000 active global wildfires!
To bypass cloud firewalls and handle messy data, we engineered a custom data-cleaning filter.
Then, we linked the coordinates to the OpenWeatherMap API to calculate real-time wind speed and direction.
Right now, the frontend map isn’t just displaying fire locations—it is actively rendering dynamic Risk Vectors (yellow trajectory lines) showing exactly where the wind is driving the fire spread.
Next up: building the AI hazard scoring system!