# LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing Source: [LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:22:26.639Z Authors: Leonard Hackel, Kai Norman Clasen, Mahdyar Ravanbakhsh, Beg\\"um Demir (1,3) \_\_INSTITUTION\_4\_\_ Technische Universit\\"at Berlin, (2) Zalando SE Berlin, (3) Berlin Institute for the Foundations of Learning, Data) Published: 2023-06-01T14:53:07.000Z ## Abstract Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image. Most of the existing methods require a large amount of computational resources, which limits their application in operational scenarios in RS. To address this issue, in this paper we present an effective lightweight transformer-based VQA in RS (LiT-4-RSVQA) architecture for efficient and accurate VQA in RS. Our architecture consists of: i) a lightweight text encoder module; ii) a lightweight image encoder module; iii) a fusion module; and iv) a classification module. The experimental results obtained on a VQA benchmark dataset demonstrate that our proposed LiT-4-RSVQA architecture provides accurate VQA results while significantly reducing the computational requirements on the executing hardware. Our code is publicly available at https://git.tu-berlin.de/rsim/lit4rsvqa. ## 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]() [PDF]()