Aims and Scope

The Photogrammetric Record is an international scholarly journal dedicated to advancing the frontiers of Photogrammetry, Remote Sensing, and Geomatics. We publish rigorous, peer-reviewed research that contributes to the theoretical, empirical, and practical understanding of these fields. By providing a platform for the exchange of ideas among researchers, practitioners, and policymakers, we seek to drive innovation, address global challenges, and make a difference. Our journal welcomes contributions in photogrammetry, 3D imaging, computer vision, laser scanning, and other related non-contact remote sensing techniques.

The Journal seeks to:

    Disseminate high-quality research and practical insights to the remote sensing and
    photogrammetry community; thus, enhancing communication among education, science, research, industry and the public.
    Foster international dialogue on the forefront issues in the fields of photogrammetry and remote sensing; thus, advancing international cooperation in these fields.
    Achieve recognition as a leading global platform for scholarly exchange on photogrammetry and remote sensing.
    Promote and defend the integrity of photogrammetry and remote sensing and their use; thus, strengthening the social impact of these fields.

Relevant topics include, but are not restricted to:

    Photogrammetric sensor calibration and characterisation
    Laser scanning (lidar)
    Image and 3D sensor technology (e.g. range cameras, natural user interface systems)
    Photogrammetric aspects of image processing (e.g. radiometric methods, feature extraction, image matching and scene classification)
    Mobile mapping and unmanned vehicular systems (UVS; UAVs)
    Registration and orientation
    Data fusion and integration of 3D and 2D datasets
    Point cloud processing
    3D modelling and reconstruction
    Algorithms and novel software
    Visualisation and virtual reality
    Terrain/object modelling and photogrammetric product generation
    Geometric sensor models
    Databases and structures for imaging and 3D modelling
    Standards and best practice for data acquisition and storage
    Change detection and monitoring, and sequence analysis

Applications of photogrammetry are numerous and far-reaching, and can include:

    topographic mapping
    industrial metrology
    CAD/CAM integration of 3D imaging methods
    spatial data acquisition for geographical information systems (GIS)
    digital cartography
    virtual reality
    visualisation
    computer vision
    robotics
    agriculture and forestry
    archaeology, cultural heritage and architecture
    engineering and industry
    entertainment
    environmental science (climate change, natural hazards)
    earth science (e.g. geology, geomorphology and geophysics)
    medicine and biometrics
    biology

Keywords

Photogrammetry, laser scanning, lidar, range imaging, optical metrology, GIS, remote sensing, digital imaging, geomatics, geomatic engineering, surveying, aerial photography, cartography, geospatial, geographical information, spatial information, 3D modelling, close range applications

Special Issue on The Multi-Modal Remote Sensing Foundation Model: Datasets, Methods, and Applications
Submission Date: 2026-05-01

In recent years, foundation model emerges as a pre-trained generic model that excels in a wide range of downstream tasks. In the remote sensing field, there is a soaring interest in exploring a comprehensive multi-modal remote sensing foundation model for various Earth Observation (EO) tasks such as image matching, scene classification, object detection, semantic segmentation, change detection, 3D reconstruction, and so forth. Generally, multi-modal remote sensing foundation models can be coarsely categorized into 5 classes: remote sensing vision foundation models; remote sensing vision-language foundation models; remote sensing vision-location foundation models; remote sensing vision-audio foundation models; remote sensing generative foundation models. Although researchers in the remote sensing and artificial intelligence domains have achieved great progress in multi-modal remote sensing foundation models, how to make them effectively bring kinds of EO tasks forward deserves extensive research.

The Photogrammetric Record is calling for submissions of original studies that describe multi-modal remote sensing foundation models in terms of architecture design, pre-training and fine-tuning. The large-scale pre-training datasets as well as general datasets of down-stream tasks are also welcomed. Reviews which are well summarized and of far-sighted prospects are also encouraged.

Topics for this call for papers include but not restricted to:

    Large-scale pre-training datasets
    General datasets of down-stream tasks
    Sensor-agnostic architecture of remote sensing foundation models
    Domain knowledge-guided pre-training methods
    Remote sensing vision foundation models
    Remote sensing vision-language foundation models
    Remote sensing vision-location foundation models
    Remote sensing generative foundation models
    Efficient fine-tuning methods of remote sensing foundation models
    Lightweighting methods of remote sensing foundation models
    Applications of remote sensing foundation models

Guest Editors:

Yansheng Li
Wuhan University
China

Rongjun Qin
The Ohio State University
USA

Jinchang Ren
Robert Gordon University
UK

Keywords: Contrastive learning; Fine-tuning methods; Generative learning; Lightweighting methods; Multi-modal remote sensing foundation model; Pre-training datasets; Pre-training methods