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# RFCP Backend - Iteration 1.3: Coverage Calculation + OSM Buildings
**Date:** January 30, 2025
**Type:** Backend Development
**Estimated:** 6-8 hours
**Location:** `/opt/rfcp/backend/`
---
## 🎯 Goal
Implement server-side coverage calculation with terrain (SRTM) and building obstacles (OpenStreetMap) for realistic urban RF propagation.
---
## 📋 Pre-reading
1. `RFCP-Backend-Roadmap-Complete.md` — Phase 2 & 3 details
2. `RFCP-Iteration-1.2-Terrain-Integration.md` — current terrain services
---
## 📊 Current State
```bash
# Backend 1.2 complete
/opt/rfcp/backend/app/
├── services/
│ ├── terrain_service.py # SRTM elevation ✅
│ └── los_service.py # Line-of-sight + Fresnel ✅
├── api/routes/
│ └── terrain.py # /elevation, /profile, /los, /fresnel ✅
```
**What's missing:**
- Building data (OSM)
- Coverage grid calculation
- Integration of terrain + buildings into RF model
---
## ✅ Tasks
### 1. Create OSM Buildings Service
**app/services/buildings_service.py:**
```python
import httpx
import asyncio
from typing import List, Optional
from pydantic import BaseModel
from functools import lru_cache
import hashlib
import json
from pathlib import Path
class Building(BaseModel):
"""Single building footprint"""
id: int
geometry: List[List[float]] # [[lon, lat], ...]
height: float # meters
levels: Optional[int] = None
building_type: Optional[str] = None
class BuildingsService:
"""
OpenStreetMap buildings via Overpass API
"""
OVERPASS_URL = "https://overpass-api.de/api/interpreter"
DEFAULT_LEVEL_HEIGHT = 3.0 # meters per floor
DEFAULT_BUILDING_HEIGHT = 9.0 # 3 floors if unknown
def __init__(self, cache_dir: str = "/opt/rfcp/backend/data/buildings"):
self.cache_dir = Path(cache_dir)
self.cache_dir.mkdir(exist_ok=True, parents=True)
self._memory_cache: dict[str, List[Building]] = {}
self._max_cache_size = 50 # bbox regions
def _bbox_key(self, min_lat: float, min_lon: float, max_lat: float, max_lon: float) -> str:
"""Generate cache key for bbox"""
# Round to 0.01 degree (~1km) grid for cache efficiency
key = f"{min_lat:.2f},{min_lon:.2f},{max_lat:.2f},{max_lon:.2f}"
return hashlib.md5(key.encode()).hexdigest()[:12]
async def fetch_buildings(
self,
min_lat: float, min_lon: float,
max_lat: float, max_lon: float,
use_cache: bool = True
) -> List[Building]:
"""
Fetch buildings in bounding box from OSM
Args:
min_lat, min_lon, max_lat, max_lon: Bounding box
use_cache: Whether to use cached results
Returns:
List of Building objects with height estimates
"""
cache_key = self._bbox_key(min_lat, min_lon, max_lat, max_lon)
# Check memory cache
if use_cache and cache_key in self._memory_cache:
return self._memory_cache[cache_key]
# Check disk cache
cache_file = self.cache_dir / f"{cache_key}.json"
if use_cache and cache_file.exists():
try:
with open(cache_file, 'r') as f:
data = json.load(f)
buildings = [Building(**b) for b in data]
self._memory_cache[cache_key] = buildings
return buildings
except Exception:
pass # Fetch fresh if cache corrupted
# Fetch from Overpass API
query = f"""
[out:json][timeout:30];
(
way["building"]({min_lat},{min_lon},{max_lat},{max_lon});
relation["building"]({min_lat},{min_lon},{max_lat},{max_lon});
);
out body;
>;
out skel qt;
"""
try:
async with httpx.AsyncClient(timeout=60.0) as client:
response = await client.post(
self.OVERPASS_URL,
data={"data": query}
)
response.raise_for_status()
data = response.json()
except Exception as e:
print(f"Overpass API error: {e}")
return []
# Parse response
buildings = self._parse_overpass_response(data)
# Cache results
if buildings:
# Disk cache
with open(cache_file, 'w') as f:
json.dump([b.model_dump() for b in buildings], f)
# Memory cache (with size limit)
if len(self._memory_cache) >= self._max_cache_size:
oldest = next(iter(self._memory_cache))
del self._memory_cache[oldest]
self._memory_cache[cache_key] = buildings
return buildings
def _parse_overpass_response(self, data: dict) -> List[Building]:
"""Parse Overpass JSON response into Building objects"""
buildings = []
# Build node lookup
nodes = {}
for element in data.get("elements", []):
if element["type"] == "node":
nodes[element["id"]] = (element["lon"], element["lat"])
# Process ways (building footprints)
for element in data.get("elements", []):
if element["type"] != "way":
continue
tags = element.get("tags", {})
if "building" not in tags:
continue
# Get geometry
geometry = []
for node_id in element.get("nodes", []):
if node_id in nodes:
geometry.append(list(nodes[node_id]))
if len(geometry) < 3:
continue # Invalid polygon
# Estimate height
height = self._estimate_height(tags)
buildings.append(Building(
id=element["id"],
geometry=geometry,
height=height,
levels=int(tags.get("building:levels", 0)) or None,
building_type=tags.get("building")
))
return buildings
def _estimate_height(self, tags: dict) -> float:
"""Estimate building height from OSM tags"""
# Explicit height tag
if "height" in tags:
try:
h = tags["height"]
# Handle "10 m" or "10m" format
if isinstance(h, str):
h = h.replace("m", "").replace(" ", "")
return float(h)
except (ValueError, TypeError):
pass
# Calculate from levels
if "building:levels" in tags:
try:
levels = int(tags["building:levels"])
return levels * self.DEFAULT_LEVEL_HEIGHT
except (ValueError, TypeError):
pass
# Default based on building type
building_type = tags.get("building", "yes")
type_heights = {
"house": 6.0,
"residential": 12.0,
"apartments": 18.0,
"commercial": 12.0,
"industrial": 8.0,
"warehouse": 6.0,
"garage": 3.0,
"shed": 2.5,
"roof": 3.0,
"church": 15.0,
"cathedral": 30.0,
"hospital": 15.0,
"school": 12.0,
"university": 15.0,
"office": 20.0,
"retail": 6.0,
}
return type_heights.get(building_type, self.DEFAULT_BUILDING_HEIGHT)
def point_in_building(self, lat: float, lon: float, building: Building) -> bool:
"""Check if point is inside building footprint (ray casting)"""
x, y = lon, lat
polygon = building.geometry
n = len(polygon)
inside = False
j = n - 1
for i in range(n):
xi, yi = polygon[i]
xj, yj = polygon[j]
if ((yi > y) != (yj > y)) and (x < (xj - xi) * (y - yi) / (yj - yi) + xi):
inside = not inside
j = i
return inside
def line_intersects_building(
self,
lat1: float, lon1: float, height1: float,
lat2: float, lon2: float, height2: float,
building: Building
) -> Optional[float]:
"""
Check if line segment intersects building
Returns:
Distance along path where intersection occurs, or None
"""
# Simplified 2D check + height comparison
# For accurate 3D intersection, would need proper ray-polygon intersection
from app.services.terrain_service import TerrainService
# Sample points along line
num_samples = 20
for i in range(num_samples):
t = i / num_samples
lat = lat1 + t * (lat2 - lat1)
lon = lon1 + t * (lon2 - lon1)
height = height1 + t * (height2 - height1)
if self.point_in_building(lat, lon, building):
# Check if signal height is below building
if height < building.height:
# Calculate distance
dist = t * TerrainService.haversine_distance(lat1, lon1, lat2, lon2)
return dist
return None
# Singleton instance
buildings_service = BuildingsService()
```
---
### 2. Create Coverage Calculation Service
**app/services/coverage_service.py:**
```python
import numpy as np
import asyncio
from typing import List, Optional, Tuple
from pydantic import BaseModel
from app.services.terrain_service import terrain_service, TerrainService
from app.services.los_service import los_service
from app.services.buildings_service import buildings_service, Building
class CoveragePoint(BaseModel):
lat: float
lon: float
rsrp: float # dBm
distance: float # meters from site
has_los: bool
terrain_loss: float # dB
building_loss: float # dB
class CoverageSettings(BaseModel):
radius: float = 10000 # meters
resolution: float = 200 # meters
min_signal: float = -120 # dBm threshold
use_terrain: bool = True
use_buildings: bool = True
class SiteParams(BaseModel):
lat: float
lon: float
height: float = 30 # antenna height meters
power: float = 43 # dBm (20W)
gain: float = 15 # dBi
frequency: float = 1800 # MHz
azimuth: Optional[float] = None # degrees, None = omni
beamwidth: Optional[float] = 65 # degrees
class CoverageService:
"""
RF Coverage calculation with terrain and buildings
"""
EARTH_RADIUS = 6371000
def __init__(self):
self.terrain = terrain_service
self.buildings = buildings_service
self.los = los_service
async def calculate_coverage(
self,
site: SiteParams,
settings: CoverageSettings
) -> List[CoveragePoint]:
"""
Calculate coverage grid for a single site
Returns list of CoveragePoint with RSRP values
"""
points = []
# Generate grid
grid = self._generate_grid(
site.lat, site.lon,
settings.radius,
settings.resolution
)
# Fetch buildings for coverage area (if enabled)
buildings = []
if settings.use_buildings:
# Calculate bbox with margin
lat_delta = settings.radius / 111000 # ~111km per degree
lon_delta = settings.radius / (111000 * np.cos(np.radians(site.lat)))
buildings = await self.buildings.fetch_buildings(
site.lat - lat_delta, site.lon - lon_delta,
site.lat + lat_delta, site.lon + lon_delta
)
# Calculate coverage for each point
for lat, lon in grid:
point = await self._calculate_point(
site, lat, lon,
settings, buildings
)
if point.rsrp >= settings.min_signal:
points.append(point)
return points
async def calculate_multi_site_coverage(
self,
sites: List[SiteParams],
settings: CoverageSettings
) -> List[CoveragePoint]:
"""
Calculate combined coverage from multiple sites
Best server (strongest signal) wins at each point
"""
if not sites:
return []
# Get all individual coverages
all_coverages = await asyncio.gather(*[
self.calculate_coverage(site, settings)
for site in sites
])
# Combine by best signal
point_map: dict[Tuple[float, float], CoveragePoint] = {}
for coverage in all_coverages:
for point in coverage:
key = (round(point.lat, 6), round(point.lon, 6))
if key not in point_map or point.rsrp > point_map[key].rsrp:
point_map[key] = point
return list(point_map.values())
def _generate_grid(
self,
center_lat: float, center_lon: float,
radius: float, resolution: float
) -> List[Tuple[float, float]]:
"""Generate coverage grid points"""
points = []
# Convert resolution to degrees
lat_step = resolution / 111000
lon_step = resolution / (111000 * np.cos(np.radians(center_lat)))
# Calculate grid bounds
lat_delta = radius / 111000
lon_delta = radius / (111000 * np.cos(np.radians(center_lat)))
lat = center_lat - lat_delta
while lat <= center_lat + lat_delta:
lon = center_lon - lon_delta
while lon <= center_lon + lon_delta:
# Check if within radius (circular, not square)
dist = TerrainService.haversine_distance(center_lat, center_lon, lat, lon)
if dist <= radius:
points.append((lat, lon))
lon += lon_step
lat += lat_step
return points
async def _calculate_point(
self,
site: SiteParams,
lat: float, lon: float,
settings: CoverageSettings,
buildings: List[Building]
) -> CoveragePoint:
"""Calculate RSRP at a single point"""
# Distance
distance = TerrainService.haversine_distance(site.lat, site.lon, lat, lon)
if distance < 1:
distance = 1 # Avoid division by zero
# Base path loss (Okumura-Hata for urban)
path_loss = self._okumura_hata(
distance, site.frequency, site.height, 1.5 # 1.5m receiver height
)
# Antenna pattern loss (if directional)
antenna_loss = 0.0
if site.azimuth is not None and site.beamwidth:
antenna_loss = self._antenna_pattern_loss(
site.lat, site.lon, lat, lon,
site.azimuth, site.beamwidth
)
# Terrain loss (LoS check)
terrain_loss = 0.0
has_los = True
if settings.use_terrain:
los_result = await self.los.check_line_of_sight(
site.lat, site.lon, site.height,
lat, lon, 1.5 # receiver at 1.5m
)
has_los = los_result["has_los"]
if not has_los:
# Add diffraction loss based on clearance
clearance = los_result["clearance"]
terrain_loss = self._diffraction_loss(clearance, site.frequency)
# Building loss
building_loss = 0.0
if settings.use_buildings and buildings:
for building in buildings:
intersection = self.buildings.line_intersects_building(
site.lat, site.lon, site.height + await self.terrain.get_elevation(site.lat, site.lon),
lat, lon, 1.5 + await self.terrain.get_elevation(lat, lon),
building
)
if intersection is not None:
# Building penetration loss (~20dB for concrete)
building_loss += 20.0
has_los = False
break # One building is enough
# Calculate RSRP
# RSRP = Tx Power + Tx Gain - Path Loss - Antenna Loss - Terrain Loss - Building Loss
rsrp = site.power + site.gain - path_loss - antenna_loss - terrain_loss - building_loss
return CoveragePoint(
lat=lat,
lon=lon,
rsrp=rsrp,
distance=distance,
has_los=has_los,
terrain_loss=terrain_loss,
building_loss=building_loss
)
def _okumura_hata(
self,
distance: float, # meters
frequency: float, # MHz
tx_height: float, # meters
rx_height: float # meters
) -> float:
"""
Okumura-Hata path loss model (urban)
Returns path loss in dB
"""
d_km = distance / 1000
if d_km < 0.1:
d_km = 0.1 # Minimum distance
# Mobile antenna height correction (urban)
a_hm = (1.1 * np.log10(frequency) - 0.7) * rx_height - (1.56 * np.log10(frequency) - 0.8)
# Path loss
L = (69.55 + 26.16 * np.log10(frequency) - 13.82 * np.log10(tx_height) - a_hm +
(44.9 - 6.55 * np.log10(tx_height)) * np.log10(d_km))
return L
def _antenna_pattern_loss(
self,
site_lat: float, site_lon: float,
point_lat: float, point_lon: float,
azimuth: float, beamwidth: float
) -> float:
"""Calculate antenna pattern attenuation"""
# Calculate bearing from site to point
bearing = self._calculate_bearing(site_lat, site_lon, point_lat, point_lon)
# Angle difference from main lobe
angle_diff = abs(bearing - azimuth)
if angle_diff > 180:
angle_diff = 360 - angle_diff
# Simple cosine pattern approximation
# 3dB beamwidth = angle where power drops to half
half_beamwidth = beamwidth / 2
if angle_diff <= half_beamwidth:
# Within main lobe - minimal loss
loss = 3 * (angle_diff / half_beamwidth) ** 2
else:
# Outside main lobe - significant loss
loss = 3 + 12 * ((angle_diff - half_beamwidth) / half_beamwidth) ** 2
loss = min(loss, 25) # Cap at 25dB (back lobe)
return loss
def _calculate_bearing(
self,
lat1: float, lon1: float,
lat2: float, lon2: float
) -> float:
"""Calculate bearing from point 1 to point 2 (degrees)"""
lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
dlon = lon2 - lon1
x = np.sin(dlon) * np.cos(lat2)
y = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(dlon)
bearing = np.degrees(np.arctan2(x, y))
return (bearing + 360) % 360
def _diffraction_loss(self, clearance: float, frequency: float) -> float:
"""
Knife-edge diffraction loss
Args:
clearance: Clearance in meters (negative = obstructed)
frequency: Frequency in MHz
Returns:
Additional loss in dB
"""
if clearance >= 0:
return 0.0 # No obstruction
# Fresnel parameter approximation
# v ≈ clearance * sqrt(2 / (λ * d))
# Simplified: use clearance directly
v = abs(clearance) / 10 # Normalize
# Knife-edge loss approximation
if v <= 0:
loss = 0
elif v < 2.4:
loss = 6.02 + 9.11 * v - 1.27 * v**2
else:
loss = 13.0 + 20 * np.log10(v)
return min(loss, 40) # Cap at 40dB
# Singleton
coverage_service = CoverageService()
```
---
### 3. Create Coverage API Routes
**app/api/routes/coverage.py:**
```python
from fastapi import APIRouter, HTTPException, BackgroundTasks
from typing import List, Optional
from pydantic import BaseModel
from app.services.coverage_service import (
coverage_service,
CoverageSettings,
SiteParams,
CoveragePoint
)
router = APIRouter()
class CoverageRequest(BaseModel):
"""Request body for coverage calculation"""
sites: List[SiteParams]
settings: CoverageSettings = CoverageSettings()
class CoverageResponse(BaseModel):
"""Coverage calculation response"""
points: List[CoveragePoint]
count: int
settings: CoverageSettings
stats: dict
@router.post("/calculate")
async def calculate_coverage(request: CoverageRequest) -> CoverageResponse:
"""
Calculate RF coverage for one or more sites
Returns grid of RSRP values with terrain and building effects
"""
if not request.sites:
raise HTTPException(400, "At least one site required")
if len(request.sites) > 10:
raise HTTPException(400, "Maximum 10 sites per request")
# Validate settings
if request.settings.radius > 50000:
raise HTTPException(400, "Maximum radius 50km")
if request.settings.resolution < 50:
raise HTTPException(400, "Minimum resolution 50m")
# Calculate
if len(request.sites) == 1:
points = await coverage_service.calculate_coverage(
request.sites[0],
request.settings
)
else:
points = await coverage_service.calculate_multi_site_coverage(
request.sites,
request.settings
)
# Calculate stats
rsrp_values = [p.rsrp for p in points]
los_count = sum(1 for p in points if p.has_los)
stats = {
"min_rsrp": min(rsrp_values) if rsrp_values else 0,
"max_rsrp": max(rsrp_values) if rsrp_values else 0,
"avg_rsrp": sum(rsrp_values) / len(rsrp_values) if rsrp_values else 0,
"los_percentage": (los_count / len(points) * 100) if points else 0,
"points_with_buildings": sum(1 for p in points if p.building_loss > 0),
"points_with_terrain_loss": sum(1 for p in points if p.terrain_loss > 0),
}
return CoverageResponse(
points=points,
count=len(points),
settings=request.settings,
stats=stats
)
@router.get("/buildings")
async def get_buildings(
min_lat: float,
min_lon: float,
max_lat: float,
max_lon: float
):
"""
Get buildings in bounding box (for debugging/visualization)
"""
from app.services.buildings_service import buildings_service
# Limit bbox size
if (max_lat - min_lat) > 0.1 or (max_lon - min_lon) > 0.1:
raise HTTPException(400, "Bbox too large (max 0.1 degrees)")
buildings = await buildings_service.fetch_buildings(
min_lat, min_lon, max_lat, max_lon
)
return {
"count": len(buildings),
"buildings": [b.model_dump() for b in buildings]
}
```
---
### 4. Register Routes
**Update app/main.py:**
```python
# Add import
from app.api.routes import health, projects, terrain, coverage
# Add router
app.include_router(coverage.router, prefix="/api/coverage", tags=["coverage"])
```
**Update version:**
```python
version="1.3.0"
```
---
### 5. Create Buildings Cache Directory
```bash
mkdir -p /opt/rfcp/backend/data/buildings
```
---
### 6. Test
```bash
# Restart
sudo systemctl restart rfcp-backend
# Test buildings endpoint
curl "https://api.rfcp.eliah.one/api/coverage/buildings?min_lat=48.45&min_lon=35.0&max_lat=48.47&max_lon=35.02"
# Test coverage calculation (single site)
curl -X POST "https://api.rfcp.eliah.one/api/coverage/calculate" \
-H "Content-Type: application/json" \
-d '{
"sites": [{
"lat": 48.46,
"lon": 35.05,
"height": 30,
"power": 43,
"gain": 15,
"frequency": 1800
}],
"settings": {
"radius": 2000,
"resolution": 100,
"use_terrain": true,
"use_buildings": true
}
}'
```
---
## ✅ Success Criteria
- [ ] `/api/coverage/buildings` returns OSM buildings with heights
- [ ] Buildings cached to disk (check `/opt/rfcp/backend/data/buildings/`)
- [ ] `/api/coverage/calculate` returns coverage grid
- [ ] Response includes `terrain_loss` and `building_loss` per point
- [ ] Stats show `los_percentage` and building/terrain impact
- [ ] Swagger docs show new endpoints
---
## 📁 Files Created
```
app/services/
├── buildings_service.py # NEW - OSM Overpass integration
└── coverage_service.py # NEW - RF coverage calculation
app/api/routes/
└── coverage.py # NEW - API endpoints
data/buildings/
└── *.json # Cached building data per bbox
```
---
## 📝 Notes
- Overpass API has rate limits (~10k requests/day) — caching critical
- Building height estimation: `levels × 3m` or defaults by type
- Building penetration loss: ~20dB for concrete (simplified)
- Diffraction uses knife-edge approximation
- Coverage calculation can be slow for large areas — consider async/background tasks later
---
## 🔜 Next: Iteration 1.4
- Frontend integration (replace browser calculation with API)
- Real-time coverage updates
- Progress indication for large calculations
---
**Ready for Claude Code** 🚀