Complete full PRD + architecture + folder structure + sprint-wise implementation plan (Month 1 → Month 6) for the project.

Hyperlocal Heat Intelligence Platform (HHIP)

Version: 1.0
Duration: Month 1 → Month 6 MVP
Goal: Build an AI-powered hyperlocal heat intelligence platform capable of 100m street-level heat prediction, risk mapping, alerts, and decision support for municipalities and disaster response teams.

1. Product Requirements Document (PRD)

1.1 Product Vision

Build a city-scale climate intelligence platform that predicts street-level heat risk before dangerous conditions occur.

Platform outputs:

1.2 Problem Statement

Current weather systems operate at:

5–25 km resolution

Cities need:

100 m street resolution

to predict:

1.3 Stakeholders

Primary

Municipal corporations

Disaster management authorities

Public health departments

Urban planning teams

Smart city missions

Hospitals

Emergency teams

Secondary

Climate researchers

Infrastructure operators

Construction companies

Insurance

Real estate

NGOs

1.4 Users

City Administrator

Needs:

Heat hotspots
Ward heat status
Alerts
Historical trends

Public Health Officer

Needs:

Risk groups
Heat illness prediction
Hospital load estimation

Urban Planner

Needs:

Tree deficit maps
Urban heat island analysis
Infrastructure stress

Disaster Officer

Needs:

Early warnings
Critical infrastructure status
Response recommendations

2. Functional Requirements

FR-1 Data Ingestion

Input:

Weather APIs
Satellite imagery
GIS layers
IoT sensors
Population datasets
Historical climate archives

Output:

Unified climate feature stream.

FR-2 Hyperlocal Grid Engine

Generate:

{
  "cell_id":"h3_xxx",
  "lat":13.08,
  "lon":80.27,
  "temp":41,
  "risk":"HIGH"
}

Resolution: 100m.

FR-3 Physics Engine

Models:

Radiation
Surface heating
Urban canyon
Vegetation cooling
Heat retention
Wind approximation

Outputs:

Urban Heat Index
Surface temperature
Heat storage coefficient

FR-4 AI Prediction

Forecast windows:

1 hr
3 hr
6 hr
12 hr
24 hr

Prediction:

Heat intensity
Risk
Confidence

FR-5 Alert Engine

Rules:

IF temp > 44
AND humidity > 70
AND population_density > threshold
→ ALERT

Channels:

Dashboard
SMS
API
Email
WhatsApp

FR-6 Dashboard

Views:

Live heat map
Timeline replay
Risk layers
Heat island view
Sensor layer
Forecast slider
Infrastructure layer

FR-7 Reporting

Generate:

Ward reports
Heat event reports
Daily summaries
Prediction accuracy reports

3. Non Functional Requirements

Availability: 99.5%

Inference latency: <2 sec

Heat update interval: 5 min

Prediction: 1–24 hr

Scalability: 10M grid cells

Retention: 5 years

4. System Architecture

               DATA SOURCES
 ┌───────────────────────────────────┐
 │ Weather APIs                      │
 │ Satellite                         │
 │ GIS Layers                        │
 │ IoT Sensors                       │
 │ Population Data                   │
 └──────────────┬────────────────────┘
                │
                ▼

        INGESTION + STREAMING
 ┌─────────────────────────────┐
 │ Kafka / Redpanda            │
 │ ETL                         │
 │ Validation                  │
 └──────────────┬──────────────┘
                │
                ▼

          FEATURE STORE
 ┌─────────────────────────────┐
 │ Weather Features            │
 │ GIS Features                │
 │ Physics Features            │
 └──────────────┬──────────────┘
                │
        ┌───────┴────────┐
        ▼                ▼

PHYSICS ENGINE      AI ENGINE
Heat Models         Forecasting
Radiation           Risk Model
Cooling             Spatial Model

        └───────┬────────┘
                ▼

      RISK + ALERT ENGINE

                ▼

       POSTGIS + TIMESCALE

                ▼

            FASTAPI

                ▼

React + DeckGL Dashboard

5. Repository Structure

hyperlocal-heat-intelligence/
├── backend/
├── frontend/
├── infra/
├── data/
├── ml/
├── physics/
├── docs/
├── tests/
└── scripts/

Backend

backend/
├── api/
├── routes/
│   ├── heat.py
│   ├── alerts.py
│   ├── sensors.py
│   └── prediction.py
├── services/
│   ├── ingestion/
│   ├── forecasting/
│   ├── risk/
│   └── alerts/
├── models/
├── utils/
└── main.py

Frontend

frontend/
├── src/
│   ├── components/
│   │   ├── HeatMap/
│   │   ├── Forecast/
│   │   ├── Timeline/
│   │   ├── Alerts/
│   │   └── Layers/
│   ├── pages/
│   │   ├── Dashboard/
│   │   └── Reports/
│   ├── hooks/
│   ├── services/
│   └── store/

Physics

physics/
├── radiation/
├── solar_gain.py
├── urban_canyon.py
├── surface_heating.py
├── wind_flow.py
├── cooling.py
└── uhi.py

ML

ml/
├── forecast/
│   ├── train.py
│   └── predict.py
├── risk/
├── xgboost/
├── lstm/
├── gnn/
├── feature_store/
└── notebooks/

Infra

infra/
├── docker/
├── k8s/
├── terraform/
├── monitoring/
│   ├── grafana/
│   └── prometheus/

Data

data/
├── raw/
│   ├── weather/
│   ├── satellite/
│   └── gis/
├── processed/
├── features/
└── models/

6. Database Design

Grid Table

heat_grid
---------
cell_id
lat
lon
timestamp
surface_temp
air_temp
humidity
risk
confidence

Sensor Table

sensors
-------
sensor_id
lat
lon
temp
humidity
status

Predictions

prediction
----------
cell_id
forecast_time
predicted_temp
risk
confidence
model_version

Alerts

alerts
------
alert_id
cell_id
severity
reason
issued_time

Month-wise Sprint Plan

Month 1: Data foundation

Sprint 1

Repo setup
CI/CD
Docker
Coding standards
Architecture docs

Deliverable: Running environment.

Sprint 2

Weather ingestion
Satellite ingestion
GIS import
Validation pipeline

Deliverable: Raw climate pipeline.

Sprint 3

PostGIS
Timescale
Grid generation
H3 integration

Deliverable: 100m city grid.

Month 2: Physics baseline

Sprint 4

Solar radiation
Surface heating
Albedo

Deliverable: Heat estimation.

Sprint 5

Vegetation cooling
Urban heat island
Heat storage

Deliverable: Physics engine v1.

Sprint 6

Wind approximation
Validation
Visualization

Deliverable: Physics dashboard.

Month 3: AI Engine

Sprint 7

Feature engineering
Historical preparation
Training dataset

Deliverable: ML-ready dataset.

Sprint 8

LSTM forecast
XGBoost risk

Deliverable: Heat prediction.

Sprint 9

Spatial interpolation
Grid filling

Deliverable: Complete heat map.

Month 4: Risk + Alerts

Sprint 10

Risk engine
Thresholds
Severity

Deliverable: Risk scoring.

Sprint 11

Alert engine
SMS
WhatsApp
Email

Deliverable: Alert pipeline.

Sprint 12

Population layers
Hospitals
Schools

Deliverable: Vulnerability map.

Month 5: Dashboard

Sprint 13

Map rendering
DeckGL
Heat layers

Deliverable: Live map.

Sprint 14

Timeline replay
Forecast slider
Historical playback

Deliverable: Forecast UI.

Sprint 15

Reports
Exports
Analytics

Deliverable: Reporting.

Month 6: Pilot Deployment

Sprint 16

Performance tuning
Caching
Redis

Deliverable: Optimization.

Sprint 17

Validation:
MAE
RMSE
Spatial accuracy

Deliverable: Accuracy report.

Sprint 18

Pilot deployment:
Chennai
5 wards
100m grid
24 hr prediction

Deliverable: MVP.

Success Metrics

Risks

MVP Stack Final

Frontend: React + TS + DeckGL + Mapbox

Backend: FastAPI + Celery

DB: PostGIS + Timescale

Streaming: Kafka

ML: PyTorch + XGBoost

Geo: H3 + GDAL + Rasterio

Infra: Docker + Terraform + Prometheus + Grafana

Recommended pilot:
Chennai → 5 wards → 100m → 24 hr forecast → municipal validation
before city-wide expansion.