Digitizing Property Tax Collection with Satellite-Based Computer Vision

Governments cannot tax properties they do not know exist. Manual surveys miss buildings, field workflows rely on paper, and supervisors lack real-time visibility. Red Buffer built an AI system that detects properties from satellite imagery, assigns them to field officers automatically, and tracks collection progress in real time.

Outcome

A computer vision platform that identifies properties from satellite images, powers a geo-enabled mobile app for field staff, and automates tax officer assignments modernizing the entire collection pipeline.

Increased Property Detection Rates

Automated identification reduced underestimation and expanded the taxable property base.

Faster, More Accurate Field Data

Mobile app replaced paper-based workflows reducing errors and accelerating property data capture.

Real-Time Supervisory Visibility

Dashboards provided supervisors with property detection insights, performance metrics, and tax assessment tracking.

Streamlined Collection Workflows

Automated property assignment improved efficiency and accuracy across the tax collection cycle.

ROLE

AI model training using Detectron 2 instance segmentation, mobile field application development, machine learning powered task assignment, and real-time reporting dashboard.

TOOL

Detectron 2, Mapbox, Custom IOU, Python, ML models, Mobile App (Android), Geo-location APIs, Web Dashboard, Analytics

DURATION

Multi-phase engagement with initial deployment within 6 months and continuous updates every six months.

Our Approach

  1. Satellite-Based Property Detection

    Trained a Detectron 2 instance segmentation model on Mapbox imagery with custom IOU logic to identify and classify residential, commercial, and mixed-use properties generating updated digital footprints every six months.

  2. Geo-Enabled Mobile Field App

    Built an Android application for field staff to capture property data, photos, and GPS coordinates in real time eliminating redundant manual entry and reducing errors during collection.

  3. ML-Powered Task Assignment

    Designed an automated allocation system that assigns detected properties to Excise Tax Officers based on geography and property type replacing manual distribution with intelligent routing.

  4. Real-Time Reporting Dashboard

    Implemented a web-based analytics dashboard showing property detection insights, tax assessment progress, and officer performance metrics giving supervisors operational visibility.

Why It Matters

This project combined satellite imagery analysis with mobile field tools and automated workflow management to close the gap between what exists and what gets taxed. The pattern of detecting assets from aerial imagery, verifying them on the ground with mobile tools, and tracking progress through dashboards applies to any jurisdiction modernizing property assessment, land use monitoring, or infrastructure planning.

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