# In-field Calibration of Low-Cost Sensors through XGBoost & Aggregate Sensor Data

arXiv: https://arxiv.org/abs/2506.15840 · HTML: https://mlsanity.com/paper/2506.15840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 60 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions](https://mlsanity.com/paper/2407.12647/) (2024)
- [Survey of City-Wide Homelessness Detection Through Environmental Sensing](https://mlsanity.com/paper/2503.11727/) (2025)

## Built on by (most cited first, 1 of 1)

- [A temporal deep learning framework for calibration of low-cost air quality sensors](https://mlsanity.com/paper/2604.21527/) (2026)

## Abstract (arXiv)

Effective large-scale air quality monitoring necessitates distributed sensing due to the pervasive and harmful nature of particulate matter (PM), particularly in urban environments. However, precision comes at a cost: highly accurate sensors are expensive, limiting the spatial deployments and thus their coverage. As a result, low-cost sensors have become popular, though they are prone to drift caused by environmental sensitivity and manufacturing variability. This paper presents a model for in-field sensor calibration using XGBoost ensemble learning to consolidate data from neighboring sensors. This approach reduces dependence on the presumed accuracy of individual sensors and improves generalization across different locations.