English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the key challenges in the semantic mapping problem in postdisasterenvironments is how to analyze a large amount of data efficiently with minimalsupervision. To address this challenge, we propose a deep learning-basedsemantic mapping tool consisting of three main ideas. First, we develop afrugal semantic segmentation algorithm that uses only a small amount of labeleddata. Next, we investigate on the problem of learning to detect a new class ofobject using just a few training examples. Finally, we develop an explainablecost map learning algorithm that can be quickly trained to generatetraversability cost maps using only raw sensor data such as aerial-viewimagery. This paper presents an overview of the proposed idea and the lessonslearned.

Explainable Semantic Mapping for First Responders