Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately 5× 5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.

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Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration@en

Snelle stedelijke groei, slimme energievoorziening, schonere mobiliteit en landrechten voor iedereen zijn uitdagingen die een innovatieve geomatics-aanpak vereisen. Sensortechnologieën, ruimtelijke gegevensverwerkingsmethoden en aanverwante benaderingen komen steeds meer en beter beschikbaar. De vraag is echter hoe ze te gebruiken en te verbeteren voor de toekomstbestendige Smart Surveyor? Hoe kunnen de nieuwste geomatics ontwikkelingen hieraan bijdragen? Om deze vragen te beantwoorden, werden de artikelen in de FIG-2020 proceedings geanalyseerd door de auteurs. Een overzicht van de belangrijkste trends.@en

Some of the challenges demanding innovative surveying approaches and technologies are rapid urban growth, smart energy, cleaner mobility, and ‘land rights for all’. Sensing technologies, spatial data processing methods, and related approaches are already available. However, the question is how to use and improve them to become future proof, smart surveyors? Only ten years left to achieve the SDGs for a sustainable future. How can smart surveying contribute? To answer these questions, the papers of the FIG proceedings were analysed by the author-team.In this short article, we present the main trends distilled and the papers we refer to, represent these main trends. We start with a brief review of the Smart Surveyors keynote presentations, followed by main trends described in the regular papers, and finally, a short summary of the impact of the FIG community.@en

The annual 3D GeoInfo Conference aims at bringing together international state-of-the-art research and facilitating dialogue on emerging topics in the field of 3D geoinformation. On the 1st and 2nd of October 2018, the 13th 3D GeoInfo conference was organised at the Delft University of Technology. On this occasion, the topics included 3D data collection and modelling, reconstruction methods for 3D representation, data management for maintenance of 3D geoinformation or 3D data, applications of 3D geoinformation, and visualisation. This volume of the ISPRS Archives is composed of 9 full papers that received positive double-blind peer reviews, as well as the 24 papers that received positive double-blind reviews as abstracts and were then extended into papers. The 12 full papers with the most positive reviews were instead published in the ISPRS Annals. We hope that the papers in this special issue will inspire decision-makers, academics, engineers, computer scientists, land surveyors, urban planners, and students interested in the 3D geoinformation domain. We would like to thank all the authors, the reviewers, and the organising committee for their valuable contribution towards this publication. We would like to acknowledge that we have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 677312 UMnD: Urban modelling in higher dimensions).@en

The annual 3D GeoInfo Conference aims at bringing together international state-of-the-art research and facilitating dialogue on emerging topics in the field of 3D geoinformation. On the 1st and 2nd of October 2018, the 13th 3D GeoInfo conference was organised at the Delft University of Technology. On this occasion, the topics included 3D data collection and modelling, reconstruction methods for 3D representation, data management for maintenance of 3D geoinformation or 3D data, applications of 3D geoinformation, and visualisation. This volume of the ISPRS Annals contains the 12 full papers that received the most positive double-blind peer reviews from the Scientific Committee of the 3D GeoInfo Conference. The other 33 presented papers are published in the ISPRS Archives. We hope that the papers in this special issue will inspire decision-makers, academics, engineers, computer scientists, land surveyors, urban planners, and students interested in the 3D geoinformation domain. We would like to thank all the authors, the reviewers, and the organising committee for their valuable contribution towards this publication. We would like to acknowledge that we have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 677312 UMnD: Urban modelling in higher dimensions).@en