JTJoseph Tuyishimire

Joseph Tuyishimire

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


Joseph Tuyishimire has a total of 17 co-authors. Here are the top co-authors by number of shared publications:

  • Athanase Hafashimana

    AIMS Research and Innovation Centre, University of Rwanda, Kigali, Rwanda

    1 shared publication
  • Boaz Mwubahimana

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China

    1 shared publication
  • Dingruibo Miao

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China

    1 shared publication
  • Diogene Tuyizere

    Biology Department, School of Sciences, College of Science and Technology University of Rwanda Huye Rwanda

    1 shared publication
  • Elias Nyandwi

    Center for Geographic Information Systems and Remote Sensing (CGIS), College of Science and Technology, University of Rwanda, Kigali, Rwanda

    1 shared publication
  • Eric Habineza

    Center for Geographic Information Systems and Remote Sensing (CGIS), College of Science and Technology, University of Rwanda, Kigali, Rwanda

    1 shared publication

Latest Publications

  • C2FNet: Cross-Probabilistic Weak Supervision Learning for High-Resolution Land Cover Enhancement

    by Boaz Mwubahimana, Yan Jianguo, Dingruibo Miao, Zhuohong Li, Haonan Guo, Le Ma, Maurice Mugabowindekwe, Swalpa Kumar Roy, Xiao Huang, Elias Nyandwi, et al.

    Autonomous large-scale high-resolution land cover (HRLC) mapping remains a major challenge in remote sensing due to the scarcity of reliable training data and resolution mismatches between available labels and input of massive of emerging imagery. Existing global land cover products often suffer from coarse spatial resolution and label noise, limiting their utility for fine-scale urban analysis and environmental monitoring. This paper presents C2FNet, a novel Coarse-to-Fine Network designed to generate HRLC maps from noisy, coarse-resolution labels using a weak supervision strategy of the cross-probability. The 2FNet consists of three key modules: (1) Edge Resolution Refinement Backbones (ERRB), which preserve spatial detail via multiscale feature extraction through parallel convolutional branches; (2) Unsupervised Dynamic Shuffle and Diagonal Annotation (UDSDA), which enhances training reliability by identifying confident regions through spatial-consistency analysis and confidence estimation; and (3) a Contrasting Self-Supervised Loss (C2F-Loss) that integrates cross-entropy and cosine similarity terms to mitigate supervision noise and resolution gaps. Evaluations of three benchmark data sets that encompass diverse urban and rural landscapes show that C2FNet achieves state of the art performance, with 80. 01% overall accuracy and a Cohen’s kappa score of 0.7567, outperforming state-of-the-art (SoA) models with weak supervision. Dataset and code: Available here.


  • Preliminary evidence of upland plant encroachment in high‐elevation wetlands of Volcanoes National Park, Rwanda

    by Diogene Tuyizere, Joseph Tuyishimire, Marie Fidèle Tuyisenge, Yntze van der Hoek

    Wetland ecosystems are increasingly altered by anthropogenic activities, with upland plant encroachment as one possible outcome. Here, we present preliminary evidence that encroachment by herbaceous and woody upland species is ongoing in five small Afromontane wetlands straddling high elevations (~2600–3400 m a.s.l.) in Volcanoes National Park (Rwanda). Although encroachment seems to commence at wetland edges it reaches waterlogged soils at wetland centres. This evidence of the spread of upland plants in these wetlands should be followed by additional research efforts focused on possible causes and consequences of this encroachment as well as the exploration of potential management and mitigation efforts.