Path Planning from Satellite Images

Training Data

Two open access geospatial imaging datasets have been used to train the segmentation models:

1. Massachusetts Roads Dataset
   • 1438 images with masks
   • 1500x1500 resolution
   • Urban terrain
2. DeepGlobe Road Extraction Dataset
   • 6226 images with masks
   • 1024x1024 resolution
   • Semi-Urban & Rural terrain

Model Architecture

Two models have been used to train on respective datasets, imported from segmentation_models_pytorch:

1. UNet
   • Encoder: ResNet-34 pretrained on imagenet
   • Activation : Sigmoid
   • Loss fn : DiceLoss , Optimizer : Adam(lr=4e-5)
   • Batch Size : 16, epochs : 6
   • Dataset : Massachusetts
2. DeepLabV3+
   • Encoder : ResNet-50 pretrained on imagenet
   • Activation : Sigmoid
   • Loss fn : DiceLoss , Optimizer : Adam(lr=8e-5)
   • Batch Size : 4, epochs : 4
   • Dataset : DeepGlobe

Algorithms

1. Image Processing
   • opening : erosion + dilation
   • closing : dilation + erosion
   • Skeletonize (scikit-image)
2. Route Finding
   • BFS : considering adjacent pixels of same color as connected
   • Dijkstra : using [0.1*d + sum{(c1-c2)^2}] as weights of edges followed by pruning of redundant nodes
   • A* : using a heuristic that underestimates the distance to the goal

Workflow

Input Satellite Image	→	Generate Road Mask	→	Skeletonize Road Map
				↓
Shortest Road Path	←	Prune & Smoothen Path	←	Find Shortest Path