---

name: urban-remote-sensing description: > Use this Skill for urban remote sensing: impervious surface mapping, NDVI urban heat island, building footprint extraction, and land use change detection. tags:

• urban-science
• remote-sensing
• satellite
• urban-heat-island
• land-use version: "1.0.0" authors:
• name: Rosetta Skills Contributors github: "@xjtulyc" license: "MIT" platforms:
• claude-code
• codex
• gemini-cli
• cursor dependencies: python:
  • numpy>=1.24
  • scipy>=1.11
  • scikit-learn>=1.3
  • matplotlib>=3.7
  • rasterio>=1.3
  • geopandas>=0.14 last_updated: "2026-03-17" status: "stable"

---

Urban Remote Sensing Analysis

One-line summary: Analyze urban land cover from satellite imagery: impervious surface mapping, NDVI-based urban heat island, LULC classification, and spectral change detection using scikit-learn and rasterio.

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When to Use This Skill

• When mapping impervious surfaces and urban expansion from multispectral imagery
• When analyzing urban heat island effects using LST and NDVI correlation
• When classifying urban land use/land cover with random forest or SVM
• When detecting urban change between two time periods
• When computing urban spatial metrics (patch density, fragmentation, edge density)
• When analyzing building shadows and 3D urban form from satellite data

Trigger keywords: urban remote sensing, impervious surface, urban heat island, NDVI urban, LULC classification, land use change, satellite urban, building extraction, urban sprawl, rasterio, spectral unmixing, change detection, Landsat urban

---

Background & Key Concepts

Urban Spectral Signatures

Surface type	Red	NIR	SWIR	NDVI	NDBI
Dense vegetation	Low	High	Low	0.6–0.9	−0.5 to −0.3
Urban/impervious	Medium	Low	High	−0.1 to 0.1	0.1–0.4
Water	Low	Very low	Low	−0.3 to 0	−0.5 to −0.2
Bare soil	Medium	Medium	High	0.0–0.2	−0.1 to 0.1

Urban Heat Island (UHI)

Surface UHI = LST(urban) − LST(suburban/rural). Correlated with:

• Low NDVI (less vegetation cooling)
• High NDBI (more impervious surface)
• Building density and anthropogenic heat

LST estimated from Landsat Band 10 (TIRS):

$ T_s = \frac{B_{10} / \varepsilon^{1/4}}{1 + (\lambda \cdot B_{10} / \rho) \cdot \ln(\varepsilon)} $

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Environment Setup

Install Dependencies

pip install numpy>=1.24 scipy>=1.11 scikit-learn>=1.3 matplotlib>=3.7 \
            rasterio>=1.3 geopandas>=0.14
# For actual satellite data download:
pip install earthengine-api>=0.1.380 geemap>=0.29

Verify Installation

import numpy as np
import rasterio
from rasterio.transform import from_bounds
print(f"rasterio: {rasterio.__version__}")

# Create synthetic raster for testing
from rasterio.io import MemoryFile
import numpy as np
data = np.random.randint(0, 4000, (4, 100, 100), dtype=np.uint16)
print(f"Test raster: shape={data.shape}")

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Core Workflow

Step 1: Urban Spectral Index Computation

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

# ------------------------------------------------------------------ #
# Compute urban spectral indices from multispectral imagery
# Simulates Sentinel-2 / Landsat 8 bands
# ------------------------------------------------------------------ #

np.random.seed(42)
SIZE = 200  # 200×200 pixel scene

# ---- Simulate a city with different land cover types ------------ #
# Create city layout: center=downtown, rings=suburban, edges=rural

y_grid, x_grid = np.mgrid[0:SIZE, 0:SIZE]
cx, cy = SIZE/2, SIZE/2
dist_from_center = np.sqrt((x_grid - cx)**2 + (y_grid - cy)**2)

# Land cover map based on distance zones
# 0=downtown, 1=residential, 2=industrial, 3=urban park, 4=suburban, 5=water, 6=agriculture
lc_map = np.zeros((SIZE, SIZE), dtype=int)
lc_map[dist_from_center < 30]  = 0  # Downtown core
lc_map[(dist_from_center >= 30) & (dist_from_center < 60)] = 1   # Residential
lc_map[(dist_from_center >= 60) & (dist_from_center < 80)] = 2   # Industrial
lc_map[(dist_from_center >= 80) & (dist_from_center < 100)] = 4  # Suburban
lc_map[dist_from_center >= 100] = 6  # Agriculture/rural

# Add parks (circles within residential zone)
for px, py in [(50, 80), (150, 70), (80, 140)]:
    mask = (np.sqrt((x_grid-px)**2 + (y_grid-py)**2) < 15)
    lc_map[mask & (dist_from_center < 100)] = 3

# Add water body
lc_map[170:190, 10:50] = 5
lc_map[100:130, 160:190] = 5

# ---- Spectral signatures per class (Sentinel-2 surface reflectance, ×1e4)
signatures = {
    0: {'B4': 1800, 'B8': 1500, 'B11': 2200, 'B10': 320},  # Downtown (high NDBI)
    1: {'B4': 1500, 'B8': 2800, 'B11': 1800, 'B10': 295},  # Residential
    2: {'B4': 2000, 'B8': 1200, 'B11': 2500, 'B10': 310},  # Industrial
    3: {'B4': 800,  'B8': 5500, 'B11': 900,  'B10': 275},  # Urban park
    4: {'B4': 1200, 'B8': 3500, 'B11': 1500, 'B10': 285},  # Suburban
    5: {'B4': 600,  'B8': 400,  'B11': 300,  'B10': 270},  # Water
    6: {'B4': 900,  'B8': 4500, 'B11': 1200, 'B10': 278},  # Agriculture
}
sig_names = ['Downtown', 'Residential', 'Industrial', 'Urban park', 'Suburban', 'Water', 'Agriculture']

# Generate band images with noise
def make_band(lc, sig_key, noise=150):
    band = np.zeros_like(lc, dtype=float)
    for cls, sig in signatures.items():
        band[lc == cls] = sig[sig_key]
    band += np.random.randn(*band.shape) * noise
    return np.clip(band, 0, 10000)

B4  = make_band(lc_map, 'B4')   # Red
B8  = make_band(lc_map, 'B8')   # NIR
B11 = make_band(lc_map, 'B11')  # SWIR
LST_sim = make_band(lc_map, 'B10')  # Simulated LST (K)

# ---- Spectral indices ------------------------------------------- #
NDVI = (B8 - B4) / (B8 + B4 + 1e-6)   # Vegetation
NDBI = (B11 - B8) / (B11 + B8 + 1e-6)  # Built-up
NDWI = (B4 - B11) / (B4 + B11 + 1e-6)  # Water (MNDWI variant)

# Impervious surface proxy: high NDBI, low NDVI
impervious_mask = (NDBI > 0.05) & (NDVI < 0.2)
print(f"Impervious surface fraction: {impervious_mask.mean()*100:.1f}%")

# ---- Visualize -------------------------------------------------- #
fig, axes = plt.subplots(2, 3, figsize=(16, 10))

# Land cover map
lc_colors = ['#8B0000','#FFA500','#808080','#228B22','#90EE90','#0000FF','#90EE90']
cmap_lc = ListedColormap(lc_colors)
im0 = axes[0][0].imshow(lc_map, cmap=cmap_lc, vmin=0, vmax=6)
axes[0][0].set_title("Land Cover Map")
plt.colorbar(im0, ax=axes[0][0], ticks=range(7))
axes[0][0].set_xticks([]); axes[0][0].set_yticks([])

# NDVI
im1 = axes[0][1].imshow(NDVI, cmap='RdYlGn', vmin=-0.5, vmax=0.9)
plt.colorbar(im1, ax=axes[0][1], label='NDVI')
axes[0][1].set_title("NDVI (Vegetation)"); axes[0][1].set_xticks([]); axes[0][1].set_yticks([])

# NDBI
im2 = axes[0][2].imshow(NDBI, cmap='RdBu_r', vmin=-0.5, vmax=0.5)
plt.colorbar(im2, ax=axes[0][2], label='NDBI')
axes[0][2].set_title("NDBI (Built-up Index)"); axes[0][2].set_xticks([]); axes[0][2].set_yticks([])

# LST
im3 = axes[1][0].imshow(LST_sim, cmap='hot', vmin=265, vmax=335)
plt.colorbar(im3, ax=axes[1][0], label='LST (K)')
axes[1][0].set_title("Land Surface Temperature"); axes[1][0].set_xticks([]); axes[1][0].set_yticks([])

# Impervious surface
im4 = axes[1][1].imshow(impervious_mask.astype(float), cmap='Reds', vmin=0, vmax=1)
plt.colorbar(im4, ax=axes[1][1], label='Impervious (1=yes)')
axes[1][1].set_title(f"Impervious Surface ({impervious_mask.mean()*100:.1f}%)")
axes[1][1].set_xticks([]); axes[1][1].set_yticks([])

# UHI: NDVI vs LST scatter
ndvi_flat = NDVI.flatten()
lst_flat  = LST_sim.flatten()
im5 = axes[1][2].scatter(ndvi_flat[::20], lst_flat[::20], c=lc_map.flatten()[::20],
                          cmap=cmap_lc, vmin=0, vmax=6, s=5, alpha=0.5)
from scipy.stats import pearsonr
r, p = pearsonr(ndvi_flat, lst_flat)
axes[1][2].set_xlabel("NDVI"); axes[1][2].set_ylabel("LST (K)")
axes[1][2].set_title(f"UHI: NDVI vs. LST\n(r={r:.3f}, p={p:.2e})")
axes[1][2].grid(True, alpha=0.3)

plt.suptitle("Urban Remote Sensing Analysis — Spectral Indices and UHI")
plt.tight_layout()
plt.savefig("urban_spectral_analysis.png", dpi=150)
plt.show()

Step 2: Urban LULC Classification

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score

# ------------------------------------------------------------------ #
# Supervised LULC classification using spectral features
# ------------------------------------------------------------------ #

# Build feature matrix: band values + derived indices
features = np.column_stack([
    B4.flatten(), B8.flatten(), B11.flatten(),
    NDVI.flatten(), NDBI.flatten(), NDWI.flatten(),
    LST_sim.flatten(),
])
labels = lc_map.flatten()

# Train/test split (stratified)
X_train, X_test, y_train, y_test = train_test_split(
    features, labels, test_size=0.25, random_state=42, stratify=labels
)

# Random Forest classifier
rf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
y_pred_prob = rf.predict_proba(X_test)

acc = accuracy_score(y_test, y_pred)
print(f"Overall accuracy: {acc*100:.2f}%")
print(classification_report(y_test, y_pred, target_names=sig_names, zero_division=0))

# Feature importance
fi = dict(zip(['B4','B8','B11','NDVI','NDBI','NDWI','LST'], rf.feature_importances_))
print("\nFeature importances:")
for feat, imp in sorted(fi.items(), key=lambda x: -x[1]):
    print(f"  {feat}: {imp:.4f}")

# Classify full image
classified_map = rf.predict(features).reshape(SIZE, SIZE)

fig, axes = plt.subplots(1, 3, figsize=(16, 5))

cmap_lc = ListedColormap(['#8B0000','#FFA500','#808080','#228B22','#90EE90','#0000FF','#90EE90'])
axes[0].imshow(lc_map, cmap=cmap_lc, vmin=0, vmax=6)
axes[0].set_title("True Land Cover"); axes[0].set_xticks([]); axes[0].set_yticks([])

axes[1].imshow(classified_map, cmap=cmap_lc, vmin=0, vmax=6)
axes[1].set_title(f"RF Classification (OA={acc*100:.1f}%)"); axes[1].set_xticks([]); axes[1].set_yticks([])

# Confusion heat map
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred, normalize='true')
im = axes[2].imshow(cm, cmap='Blues', vmin=0, vmax=1)
plt.colorbar(im, ax=axes[2])
axes[2].set_xticks(range(7)); axes[2].set_xticklabels(sig_names, rotation=40, ha='right', fontsize=7)
axes[2].set_yticks(range(7)); axes[2].set_yticklabels(sig_names, fontsize=7)
axes[2].set_title("Confusion Matrix (normalized)"); axes[2].set_xlabel("Predicted"); axes[2].set_ylabel("True")

plt.tight_layout()
plt.savefig("lulc_classification.png", dpi=150)
plt.show()

Step 3: Urban Heat Island Profile Analysis

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
from scipy.ndimage import gaussian_filter

# ------------------------------------------------------------------ #
# Urban heat island profile: transect and ring-average analysis
# ------------------------------------------------------------------ #

# Convert LST to Celsius
LST_C = LST_sim - 273.15

# Ring-average analysis
y_grid, x_grid = np.mgrid[0:SIZE, 0:SIZE]
cx, cy = SIZE/2, SIZE/2
dist_pix = np.sqrt((x_grid - cx)**2 + (y_grid - cy)**2)

ring_radii = np.arange(0, dist_pix.max(), 5)
ring_lst  = []
ring_ndvi = []
ring_dist = []
for r0, r1 in zip(ring_radii[:-1], ring_radii[1:]):
    mask = (dist_pix >= r0) & (dist_pix < r1)
    if mask.sum() > 0:
        ring_lst.append(LST_C[mask].mean())
        ring_ndvi.append(NDVI[mask].mean())
        ring_dist.append((r0+r1)/2)

ring_dist  = np.array(ring_dist)
ring_lst   = np.array(ring_lst)
ring_ndvi  = np.array(ring_ndvi)

# Pixel scale: assume 10m resolution
dist_km = ring_dist * 10 / 1000

uhi_intensity = ring_lst[:3].mean() - ring_lst[-5:].mean()
print(f"Urban Heat Island intensity: {uhi_intensity:.2f}°C")

# NDVI–LST correlation
r_corr, p_corr = pearsonr(NDVI.flatten(), LST_C.flatten())
print(f"NDVI–LST Pearson r = {r_corr:.4f}, p = {p_corr:.2e}")

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Ring-average LST
axes[0].plot(dist_km, ring_lst, 'r-o', linewidth=2, markersize=5)
axes[0].set_xlabel("Distance from center (km)"); axes[0].set_ylabel("Mean LST (°C)")
axes[0].set_title("Urban Heat Island Profile\n(Distance from city center)")
axes[0].grid(True, alpha=0.3)
axes[0].annotate(f"UHI intensity={uhi_intensity:.1f}°C",
                  xy=(0, ring_lst[0]), xytext=(2, ring_lst[0]+1),
                  arrowprops=dict(arrowstyle='->', color='red'), color='red')

# Ring-average NDVI
axes[1].plot(dist_km, ring_ndvi, 'g-o', linewidth=2, markersize=5)
axes[1].set_xlabel("Distance from center (km)"); axes[1].set_ylabel("Mean NDVI")
axes[1].set_title("Vegetation Gradient\n(Distance from city center)")
axes[1].grid(True, alpha=0.3)

# LST vs. NDVI scatter
sc = axes[2].scatter(NDVI.flatten()[::5], LST_C.flatten()[::5],
                      c=lc_map.flatten()[::5],
                      cmap=ListedColormap(['#8B0000','#FFA500','#808080','#228B22','#90EE90','#0000FF','#90EE90']),
                      vmin=0, vmax=6, s=2, alpha=0.3)
# Regression line
z = np.polyfit(NDVI.flatten(), LST_C.flatten(), 1)
x_fit = np.linspace(NDVI.min(), NDVI.max(), 100)
axes[2].plot(x_fit, np.polyval(z, x_fit), 'k-', linewidth=2.5, label=f"y={z[0]:.1f}x+{z[1]:.1f}")
axes[2].set_xlabel("NDVI"); axes[2].set_ylabel("LST (°C)")
axes[2].set_title(f"UHI: NDVI–LST Relationship\nr={r_corr:.3f}")
axes[2].legend(); axes[2].grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig("uhi_analysis.png", dpi=150)
plt.show()

---

Advanced Usage

Urban Expansion Change Detection

import numpy as np
import matplotlib.pyplot as plt

# ------------------------------------------------------------------ #
# Detect urban expansion between two time periods
# ------------------------------------------------------------------ #

# Simulate 5-year urban expansion (additional 15% urban growth)
np.random.seed(99)
ndbi_t1 = NDBI.copy()
ndbi_t2 = NDBI.copy()

# Expand urban fringe (add new development at suburban edge)
suburban_mask = (dist_from_center >= 75) & (dist_from_center < 95)
ndbi_t2[suburban_mask] += np.random.uniform(0.1, 0.3, suburban_mask.sum())
ndbi_t2 = np.clip(ndbi_t2, -1, 1)

# Change detection: NDBI difference
ndbi_change = ndbi_t2 - ndbi_t1
new_urban = (ndbi_change > 0.15) & (ndbi_t1 < 0.1)  # Was non-urban, now urban

new_urban_pct = new_urban.mean() * 100
print(f"New urban pixels detected: {new_urban_pct:.2f}% of scene")

fig, axes = plt.subplots(1, 3, figsize=(14, 4))
axes[0].imshow(ndbi_t1, cmap='RdBu_r', vmin=-0.5, vmax=0.5); axes[0].set_title("NDBI T1"); axes[0].axis('off')
axes[1].imshow(ndbi_t2, cmap='RdBu_r', vmin=-0.5, vmax=0.5); axes[1].set_title("NDBI T2"); axes[1].axis('off')
axes[2].imshow(new_urban.astype(float), cmap='hot', vmin=0, vmax=1)
axes[2].set_title(f"Urban Expansion ({new_urban_pct:.1f}% new)"); axes[2].axis('off')
plt.tight_layout(); plt.savefig("urban_expansion.png", dpi=150); plt.show()

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Troubleshooting

Rasterio fails to read satellite data

# Load actual Sentinel-2 / Landsat data:
import rasterio
with rasterio.open("landsat_band4.tif") as src:
    B4_real = src.read(1).astype(float)
    transform = src.transform
    crs = src.crs
    print(f"CRS: {crs}, shape: {B4_real.shape}")

Classification accuracy too low (<80%)

Fixes:

1. Add more training samples per class
2. Include texture features (GLCM)
3. Use higher spectral resolution or more bands
4. Try SVM with RBF kernel for small sample sizes

NDVI undefined (0/0 division)

NDVI = np.where((B8 + B4) > 0, (B8 - B4) / (B8 + B4), 0)

Version Compatibility

Package	Tested versions	Notes
rasterio	1.3	CRS handling changed in 1.3
geopandas	0.14	Requires shapely 2.0
scikit-learn	1.3, 1.4	RandomForest API stable

---

External Resources

Official Documentation

• rasterio documentation
• Sentinel-2 band descriptions (ESA)

Key Papers

• Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas. ISPRS Journal.
• Voogt, J.A. & Oke, T.R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment.

---

Examples

Example 1: Urban Greenness Index by Neighborhood

import numpy as np
import pandas as pd

# Compute per-zone NDVI statistics
zones_analysis = []
for cls_id, cls_name in enumerate(sig_names):
    mask = lc_map == cls_id
    if mask.sum() > 0:
        zones_analysis.append({
            'class': cls_name,
            'area_pix': mask.sum(),
            'mean_ndvi': NDVI[mask].mean(),
            'mean_lst_C': LST_C[mask].mean(),
            'pct_impervious': impervious_mask[mask].mean() * 100,
        })

df_zones = pd.DataFrame(zones_analysis)
print("Urban zone characteristics:")
print(df_zones.round(3).to_string(index=False))

Example 2: Sky View Factor (Urban Canyon)

import numpy as np
import matplotlib.pyplot as plt

# Simplified sky view factor: fraction of sky visible from ground
# High buildings in dense downtown block sky (low SVF)
svf_approx = np.exp(-0.1 * (NDBI + 0.5) * 10)  # Simplified model
svf_approx = np.clip(svf_approx, 0, 1)

fig, ax = plt.subplots(figsize=(7, 6))
im = ax.imshow(svf_approx, cmap='Blues', vmin=0, vmax=1)
plt.colorbar(im, ax=ax, label='Sky View Factor (0=blocked, 1=open)')
ax.set_title("Sky View Factor (Urban Canyon Proxy)"); ax.axis('off')
plt.tight_layout(); plt.savefig("sky_view_factor.png", dpi=150); plt.show()
print(f"Mean SVF downtown: {svf_approx[lc_map==0].mean():.3f}")
print(f"Mean SVF rural:    {svf_approx[lc_map==6].mean():.3f}")

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Last updated: 2026-03-17 | Maintainer: @xjtulyc Issues: GitHub Issues