# Explainable Semantic Mapping for First Responders

Source: [Explainable Semantic Mapping for First Responders](<https://www.sota2.com/research/paper/68fa667d1e209131cea10914>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:51:21.649Z

Authors: Jean Oh, Martial Hebert, Hae-Gon Jeon, Xavier Perez, Chia Dai, Yeeho Song

Published: 2019-10-15T22:58:19.000Z

## Abstract

One of the key challenges in the semantic mapping problem in postdisaster environments is how to analyze a large amount of data efficiently with minimal supervision. To address this challenge, we propose a deep learning-based semantic mapping tool consisting of three main ideas. First, we develop a frugal semantic segmentation algorithm that uses only a small amount of labeled data. Next, we investigate on the problem of learning to detect a new class of object using just a few training examples. Finally, we develop an explainable cost map learning algorithm that can be quickly trained to generate traversability cost maps using only raw sensor data such as aerial-view imagery. This paper presents an overview of the proposed idea and the lessons learned.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.07093>)

[PDF](<https://arxiv.org/pdf/1910.07093.pdf>)