Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images

23 August 2018

Papers citing "Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images"

8 / 8 papers shown

Title
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding Yimiao Zhou Mengcheng Lan Xiang Li Yiping Ke Yiping Ke Xue Jiang Qingyun Li Xue Yang Wayne Zhang ObjD VLM 256 7 0 16 Nov 2024
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases Chun Yang Franz Rottensteiner Christian Heipke 96 20 0 14 Apr 2021
Robust building footprint extraction from big multi-sensor data using deep competition network M. Khoshboresh-Masouleh M. Saradjian 103 9 0 04 Nov 2020
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification Danfeng Hong Lianru Gao Naoto Yokoya Jing Yao Jocelyn Chanussot Q. Du Bing Zhang 83 969 0 12 Aug 2020
Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network Kang Zhao Muhammad Kamran Gunho Sohn 116 11 0 23 Jun 2020
Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation Xiang Li Lingjing Wang Yi Fang 52 6 0 10 Jun 2020
Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF) Qingyu Li Yilei Shi Xin Huang Xiaoxiang Zhu 92 65 0 11 Feb 2020
Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution Shivangi Srivastava John E. Vargas-Muñoz D. Tuia 94 138 0 05 May 2019