# Workflow Design Analysis for High Resolution Satellite Image Analysis

## TL;DR

This paper analyzes workflow designs for processing large volumes of high-resolution satellite images using HPC, focusing on optimizing task execution, resource utilization, and pipeline efficiency in ecological monitoring applications.

## Contribution

It introduces a task-parallel, data-driven workflow design tailored for large-scale satellite image analysis on HPC systems, with experimental modeling and evaluation.

## Key findings

- Optimal workflow design identified for large-scale image processing
- Resource utilization and overheads characterized for different designs
- Model predicts execution time and efficiency for ecological image analysis

## Abstract

Ecological sciences are using imagery from a variety of sources to monitor and survey populations and ecosystems. Very High Resolution (VHR) satellite imagery provide an effective dataset for large scale surveys. Convolutional Neural Networks have successfully been employed to analyze such imagery and detect large animals. As the datasets increase in volume, O(TB), and number of images, O(1k), utilizing High Performance Computing (HPC) resources becomes necessary. In this paper, we investigate a task-parallel data-driven workflows design to support imagery analysis pipelines with heterogeneous tasks on HPC. We analyze the capabilities of each design when processing a dataset of 3,000 VHR satellite images for a total of 4~TB. We experimentally model the execution time of the tasks of the image processing pipeline. We perform experiments to characterize the resource utilization, total time to completion, and overheads of each design. Based on the model, overhead and utilization analysis, we show which design approach to is best suited in scientific pipelines with similar characteristics.