Quick Start

python depth_map.py MAP00.csv
python interpolated_map.py MAP00.csv
python contour_map.py MAP00.csv
python csv_to_kml_colored.py MAP00.csv
python google_earth_overlay.py MAP00.csv

Oceanic Measurement & Environmental Geospatial Array

A compact underwater survey system that collects depth, position, and environmental data from a moving ROV and converts those measurements into bathymetric maps and georeferenced outputs. Measurements are filtered in real time using quality and spatial constraints, producing both a complete log and a reduced mapping dataset. A continuous surface is generated from the filtered points through spatial interpolation.

Overview

The system is built around an Arduino-based capture device and a Python-based processing workflow.

It is designed to:

• record reliable field data in real time
• filter out low-quality measurements
• generate usable spatial maps from irregular samples
• visualize results as raster maps and georeferenced overlays

Repository Structure

arduino/  rov_logger_mapping.ino

docs/  SYSTEM.md  SCRIPTS.md

scripts/  csv_to_kml.py  csv_to_kml_colored.py  depth_map.py  interpolated_map.py  contour_map.py  google_earth_overlay.py

example_data/  MAP00.CSV

System Architecture

Architecture Overview

Hardware

Components

• Arduino Mega
• GPS module (UART)
• Waterproof ultrasonic distance sensor
• DS18B20 temperature sensor
• BNO085 IMU
• I2C 16×2 LCD
• SD card module

Wiring Summary

Data Pipeline

1. Data Capture

The Arduino logger writes two files:

• dataXX.csv — complete system log (diagnostics)
• mapXX.csv — filtered survey dataset

Mapping points are recorded only when the system detects:

• valid GPS fix
• acceptable HDOP
• sufficient satellite count
• recent fix age
• valid depth reading
• stable orientation (pitch/roll)
• acceptable platform speed
• minimum spacing from the previous point

2. Processing

Run scripts on the mapping dataset:

python depth_map.py MAP00.CSV
python interpolated_map.py MAP00.CSV
python contour_map.py MAP00.CSV
python google_earth_overlay.py MAP00.CSV

3. Outputs

• Scatter map — validation of raw coverage
• Interpolated map — continuous surface model
• Contour map — readable bathymetry
• KML + overlay — georeferenced visualization

Key Features

UTC Time Logging

All timestamps are recorded in UTC to eliminate timezone ambiguity.

Depth Smoothing

A moving average filter reduces sonar noise and rejects transient spikes.

Sound Speed Correction

Depth is adjusted using a temperature-based estimate of sound speed.

Real-Time Data Filtering

Measurements are evaluated during acquisition to ensure mapping data meets defined quality thresholds.

Spatial Interpolation

Inverse Distance Weighting (IDW) converts discrete samples into continuous surfaces.

Data Format

Mapping File (mapXX.csv)

point,date_utc,time_utc,lat,lng,depth_cm,temp_c,satellites,hdop,speed_kmph,fix_age_ms,pitch_deg,roll_deg,imu_acc

Outputs at a Glance

• dataXX.csv — complete acquisition log
• mapXX.csv — filtered survey dataset
• *_depth_map.png — raw point coverage
• *_interpolated.png — interpolated surface
• *_contours.png — contour visualization
• *_colored.kml — colorized Google Earth points
• *_overlay.kmz — Google Earth ground overlay

Example Workflow

python depth_map.py example_data/MAP00.CSV
python interpolated_map.py example_data/MAP00.CSV
python contour_map.py example_data/MAP00.CSV
python csv_to_kml_colored.py example_data/MAP00.CSV
python google_earth_overlay.py example_data/MAP00.CSV...