arXiv subjects

Roni Blushtein-Livnon

Publications and source records attributed to Roni Blushtein-Livnon.

6 recordsLinked to original sources

arXiv2026

Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery

Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segmentation of remote sensing (RS) imagery offers a promising solution; yet, residential PV systems remain challenging targets because of their small size and sparse distribution, resulting in severe target-background imbalance. Vision-language foundation models (FMs) provide a data-efficient paradigm through prompt-based semantic and spatial guidance, but the relative contribution of different prompt types remains unclear. We systematically evaluate SAM3 for small-scale PV segmentation in RS imagery by comparing textual, geometric, and hybrid prompting, under varying supervision levels, training strategies, spatial resolutions, and imaging conditions. Multi-temporal aerial imagery from a large off-grid rural region serves as a study site, with findings validated across three additional datasets. Prompting strategy emerged as the dominant factor governing model behavior. Textual prompting consistently produced the lowest performance and showed the greatest sensitivity to supervision and imaging conditions. In contrast, spatial guidance substantially improved both segmentation accuracy and robustness. Hybrid prompting achieved the highest accuracy and stability, indicating that semantic and spatial guidance provide complementary information. Most performance gains were achieved with only a few hundred annotated samples, demonstrating strong data efficiency. Transfer learning had limited overall impact, with only modest improvements observed for textual prompting under limited supervision. Overall, our findings establish prompting strategy as a key determinant of SAM3 adaptation, robustness, and generalization, highlighting the potential of promptable FMs for scalable PV mapping in data-constrained off-grid regions.

cs.CV↗

arXiv2026

Beyond Leaders and Laggards: A Typology of Renewable Energy Adoption Trajectories with Evidence from Off-Grid Communities

Understanding the dynamics of renewable energy adoption is essential for designing strategies that accelerate its spread - an urgent priority for advancing climate goals and improving well-being, especially in off-grid regions facing energy poverty. This study introduces a time-series-based analytical framework that quantifies and classifies adoption behaviors of geographic entities within a region. A novel metric, the Adoption over Time Index (ATI), captures cumulative adoption intensity and identifies shifts in adoption trends, improving the ability to distinguish between fundamental adoption paths. By combining ATI with three key features found to be indicative of adoption dynamics, we define a typology of eight distinct paths, including two newly identified trajectories - the decelerating path and the declining moderate path. Applying this framework to a case study of an off-grid Bedouin population in southern Israel reveals that these retreating paths exist in substantial proportions. Identifying such trends is critical for addressing stagnation and preventing backsliding in the diffusion process. The leaping path, by contrast, was nearly absent. We also identify behavioral diversity within both front-runner and trailing groups. Differentiating among these groups can help tailor acceleration strategies. The analysis further reveals significant disparities in adoption levels across the region, with lagging clusters being widespread and overall adoption falling short of the region's potential.

econ.GN↗

arXiv2026

From Expansion to Consolidation: Socio-Spatial Contagion Dynamics in Off-Grid PV Adoption

In traditional rural societies, where social ties are embedded in physical space, the diffusion of emerging technologies may be amplified through socio-spatial contagion (SSC). Such processes may play a key role in accelerating residential PV adoption in off-grid regions. Yet empirical evidence on SSC in PV adoption remains largely limited to affluent, grid-connected settings, while off-grid regions often lack systematic installation records. To address these gaps, we use a deep learning segmentation model to extract PV installations from a decade-long series of remote sensing imagery across 507 off-grid settlement clusters (hereafter, communities). This enables data-driven spatio-temporal point pattern inference of SSC in data-scarce contexts. SSC is quantified through the range and intensity of clustering of new installations around prior adopters, and the dynamics of these dimensions are linked to adoption outcomes. We found that SSC is nearly ubiquitous, often spanning most of the community's spatial extent, while exhibiting substantial heterogeneity in intensity. Although SSC intensifies over time, its effects remain temporally concentrated, peaking within 1 to 2 years of nearby installations and weakening thereafter. SSC intensity is positively associated with adoption rates in both cross-sectional and temporal analyses. However, the relationship between SSC range and adoption changes over time - in early diffusion phases, adoption growth is associated with range expansion, whereas in later phases it is associated with range contraction. This shift reflects a transition from clustering to consolidation of installations. These findings highlight the potential of seeding interventions to accelerate PV diffusion in off-grid regions.

econ.GN↗

arXiv2026

On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation

Remote sensing (RS) image segmentation is constrained by the limited availability of annotated data and a gap between overhead imagery and natural images used to train foundational models. This motivates effective adaptation under limited supervision. SAM3 concept-driven framework generates masks from textual prompts without requiring task-specific modifications, which may enable this adaptation. We evaluate SAM3 for RS imagery across four target types, comparing textual, geometric, and hybrid prompting strategies, under lightweight fine-tuning scales with increasing supervision, alongside zero-shot inference. Results show that combining semantic and geometric cues yields the highest performance across targets and metrics. Text-only prompting exhibits the lowest performance, with marked score gaps for irregularly shaped targets, reflecting limited semantic alignment between SAM3 textual representations and their overhead appearances. Nevertheless, textual prompting with light fine-tuning offers a practical performance-effort trade-off for geometrically regular and visually salient targets. Across targets, performance improves between zero-shot inference and fine-tuning, followed by diminishing returns as the supervision scale increases. Namely, a modest geometric annotation effort is sufficient for effective adaptation. A persistent gap between Precision and IoU further indicates that under-segmentation and boundary inaccuracies remain prevalent error patterns in RS tasks, particularly for irregular and less prevalent targets.

cs.CV↗

arXiv2025

Prompt-Based Segmentation at Multiple Resolutions and Lighting Conditions using Segment Anything Model 2

This paper provides insights on the effectiveness of the zero shot, prompt-based Segment Anything Model (SAM) and its updated versions, SAM 2 and SAM 2.1, along with the non-promptable conventional neural network (CNN), for segmenting solar panels in RGB aerial remote sensing imagery. The study evaluates these models across diverse lighting conditions, spatial resolutions, and prompt strategies. SAM 2 showed slight improvements over SAM, while SAM 2.1 demonstrated notable improvements, particularly in sub-optimal lighting and low resolution conditions. SAM models, when prompted by user-defined boxes, outperformed CNN in all scenarios; in particular, user-box prompts were found crucial for achieving reasonable performance in low resolution data. Additionally, under high resolution, YOLOv9 automatic prompting outperformed user-points prompting by providing reliable prompts to SAM. Under low resolution, SAM 2.1 prompted by user points showed similar performance to SAM 2.1 prompted by YOLOv9, highlighting its zero shot improvements with a single click. In high resolution with optimal lighting imagery, Eff-UNet outperformed SAMs prompted by YOLOv9, while under sub-optimal lighting conditions, Eff-UNet, and SAM 2.1 prompted by YOLOv9, had similar performance. However, SAM is more resource-intensive, and despite improved inference time of SAM 2.1, Eff-UNet is more suitable for automatic segmentation in high resolution data. This research details strengths and limitations of each model and outlines the robustness of user-prompted image segmentation models.

cs.CV↗

arXiv2024

Performance of Human Annotators in Object Detection and Segmentation of Remotely Sensed Data

This study introduces a laboratory experiment designed to assess the influence of annotation strategies, levels of imbalanced data, and prior experience, on the performance of human annotators. The experiment focuses on labeling aerial imagery, using ArcGIS Pro tools, to detect and segment small-scale photovoltaic solar panels, selected as a case study for rectangular objects. The experiment is conducted using images with a pixel size of 0.15\textbf{$m$}, involving both expert and non-expert participants, across different setup strategies and target-background ratio datasets. Our findings indicate that human annotators generally perform more effectively in object detection than in segmentation tasks. A marked tendency to commit more Type II errors (False Negatives, i.e., undetected objects) than Type I errors (False Positives, i.e. falsely detecting objects that do not exist) was observed across all experimental setups and conditions, suggesting a consistent bias in detection and segmentation processes. Performance was better in tasks with higher target-background ratios (i.e., more objects per unit area). Prior experience did not significantly impact performance and may, in some cases, even lead to overestimation in segmentation. These results provide evidence that human annotators are relatively cautious and tend to identify objects only when they are confident about them, prioritizing underestimation over overestimation. Annotators' performance is also influenced by object scarcity, showing a decline in areas with extremely imbalanced datasets and a low ratio of target-to-background. These findings may enhance annotation strategies for remote sensing research while efficient human annotators are crucial in an era characterized by growing demands for high-quality training data to improve segmentation and detection models.

cs.CV↗