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Abstract Effectively supporting the United Nations’ Sustainable Development Goals requires reliable, substantial, and timely data. For solar panel installation monitoring, where accurate reporting is crucial in tracking green energy production and sustainable energy access, official and regulated documentation remains inconsistent. Reports of solar panel installations have been supplemented with object detection models developed and used on openly available aerial imagery, a type of imagery collected by aircraft or drones and limited by cost, extent, and geographic location. We address these limitations by providing a solar panel dataset derived from 31 cm resolution satellite imagery to support rapid and accurate detection at regional and international scales. We also include complementary satellite imagery at 15.5 cm resolution with the aim of further improving solar panel detection accuracy. The dataset of 2,542 annotated solar panels may be used independently to develop detection models uniquely applicable to satellite imagery or in conjunction with existing solar panel aerial imagery datasets to support generalized detection models.

Satellite imagery provides impartial, inclusive, and timely geospatial data regarding global issues, supplementing traditional ground-based methods. The volume, velocity, and variety of satellite imagery present novel capabilities to support the United Nations' Sustainable Development Goals by providing repeatable data to assess progress and support reliable statistics connecting policy with evidence. Currently, the relationship between spatial resolution and accurate detection of sub-meter- to meter-scale objects supporting policy-making remains unexplored. This study investigates the resolution-performance trade-off by comparing results of object detection models and their implications for two sustainability-motivated use cases. These include vehicle detection, used to inform traffic flow and density for understanding infrastructure needs and lockdown policies, and solar panel detection, where accuracy is crucial in monitoring green energy production and sustainable energy access. The results show a largely linear degradation of object detection models using imagery at 30, 40, 50, and 60 cm resolutions simulated from 7 cm aerial imagery. Object detection models using 30 cm imagery improve F$_{1}$ score by 65% compared to 60 cm models. Further investigation into a preprocessing step using 30 cm imagery to generate 15 cm imagery shows an average F$_{1}$ score increase by 5% for detection of small objects across multiple use cases. The results and analysis provide a basis for more robust investigations into the capabilities of very high resolution satellite imagery to support the Sustainable Development Goals.

Comparative evaluation is a requirement for reproducible science and objective assessment of new algorithms. Reproducible research in the field of pansharpening of very high resolution images is a difficult task due to the lack of openly available reference datasets and protocols. The contribution of this article is threefold, and it defines a benchmarking framework to evaluate pansharpening algorithms. First, it establishes a reference dataset, named PAirMax, composed of 14 panchromatic and multispectral image pairs collected over heterogeneous landscapes by different satellites. Second, it standardizes various image preprocessing steps, such as filtering, upsampling, and band coregistration, by providing a reference implementation. Third, it details the quality assessment protocols for reproducible algorithm evaluation.

Accurate classification of tropical tree species is critical for understanding forest habitat, biodiversity, forest composition, biomass, and the role of trees in climate variability through carbon uptake. The aim of this study is to establish an accurate classification procedure for tropical tree species, specifically testing the feasibility of WorldView-3 (WV-3) multispectral imagery for this task. The specific study site is a defined arboretum within a well-known tropical forest research location in Costa Rica (La Selva Biological Station). An object-based classification is the basis for the analysis to classify six selected tree species. A combination of pre-processed WV-3 bands were inputs to the classification, and an edge segmentation process defined multi-pixel-scale tree canopies. WorldView-3 bands in the Green, Red, Red Edge, and Near-Infrared 2, particularly when incorporated in two specialized vegetation indices, provide high discrimination among the selected species. Classification results yield an accuracy of 85.37%, with minimal errors of commission (7.89%) and omission (14.63%). Shadowing in the satellite imagery had a significant effect on segmentation accuracy (identifying single-species canopy tops) and on classification. The methodology presented provides a path to better characterization of tropical forest species distribution and overall composition for improving biomass studies in a tropical environment.

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas.