  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.
