Spectral Imaging Deep Learning Mapper - SpecDeepMap: An open-source EnMAP-Box semantic segmentation application for hyper- and multispectral mapping
Year: 2026
Type: software-paper
Abstract: Deep learning is increasingly applied in spectral imaging and remote-sensing research, yet accessible interface-based software remains limited. We therefore developed the Spectral Imaging Deep Learning Mapper (SpecDeepMap), a free and open-source application embedded into the EnMAP-Box QGIS plugin that enables deep-learning–based spectral analysis and mapping. SpecDeepMap implements a comprehensive semantic segmentation workflow through a user-friendly graphical interface, requiring no programming expertise. The software is designed for multispectral and hyperspectral data and addresses geographical data challenges, such as spatial class distribution, and continuous largescale mapping tasks. SpecDeepMap offers various deep-learning architectures, such as U-Net, U-Net++, DeeplabV3+, and SegFormer, paired with diverse backbones such as ResNet-18, ConvNeXt, Swin-Transformers and Segment Anything Model 2. This software is the first QGIS plugin that enables fine-tuning multispectral foundation models for Sentinel-2 Top of Atmosphere Reflectance imagery. These weights stem from pretraining by Wang et al. (2022) on the Self-Supervised Learning for Earth Observation Sentinel-1/2 dataset.
Source: SoftwareX
Institutions: University of Helsinki, Humboldt-Universität zu Berlin
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
Funder: Helsingin Yliopisto
OpenAlex ID: W7118884476