arXiv subjects

Shashank Tamaskar

Publications and source records attributed to Shashank Tamaskar.

6 recordsLinked to original sources

arXiv2026

Geography as the Organizing Grammar of Geospatial Models

GeoAI increasingly produces reusable Earth representation s, physical forecasts, multimodal systems, and reasoning a gents. Their progress exposes two distinct limitations. Geo graphic completeness asks whether the represented world ex tends beyond readily observed land surfaces and atmospher ic fields to oceans, biogeography, economies, institutions, and human agency. Geographic intelligence asks whether model outputs preserve place, scale, relations, process, un certainty, and the limits of valid inference. The first con cerns what exists in a model; the second concerns what may responsibly be claimed about it. This critical integrative review argues that geography provides the organizing gram mar that connects these dimensions. Seven structural gaps, propositions, and corresponding review questions translate the argument into testable requirements. The resulting re search agenda advances Living Geospatial Models as federat ed, continually updated systems that couple specialist Earth and human-domain models through shared geographic iden tity, support, relations, provenance, and uncertainty. The objective is not a single universal network, but geospatial intelligence that can explain connections and change, antic ipate plausible futures, and support accountable decisions across places and scales.

physics.soc-ph↗

arXiv2026

AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations

Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel level supervision. This mismatch has limited the development of large-scale benchmarks for crop yield learning using multimodal Earth observation data. A reproducible benchmark, AgroBench, is presented for transforming publicly available U.S. county level crop yield statistics into weakly supervised pixel-level crop time series. Each crop pixel time series is paired with a county-level yield value as a weak supervisory signal rather than a directly measured pixel-level yield label. Our geospatial data generation pipeline integrates USDA crop yield statistics with crop-specific land cover masks, Sentinel 2 multispectral imagery, Sentinel-1 synthetic aperture radar observations, climatic variables, and terrain information to produce temporally aligned multimodal sequences describing individual crop pixels throughout the growing season. The resulting benchmark contains over 13 million observations from 788,654 unique crop pixels spanning 5,107 county year combinations across eight growing seasons (2017 to 2024) for five major U.S. crops. To facilitate standardized evaluation, we establish a crop yield prediction benchmark using a Leave-One-Year-Out evaluation protocol and provide baseline results using representative machine learning models. By releasing the complete data generation pipeline, benchmark dataset, and evaluation protocol, AgroBench provides a reproducible foundation for future research in weakly supervised learning, multimodal remote sensing, spatiotemporal modeling, and geospatial foundation models for agriculture.

cs.CV↗

arXiv2026

SPEAR NeXT Causal Latent Forecasting Across Multiple Horizons for Spectral Temporal Earth Representation Learning

Earth observation is inherently dynamic, yet temporal information in many foundation models is learned through reconstruction, invariance, or retrospective sequence summarization. SPEAR NeXT is introduced as a compact pixel-wise multimodal spectral temporal foundation model in which temporal self supervision is formulated as past only, multi horizon latent Earth state prediction. Instantaneous states are first encoded by the pretrained SPEAR model from optical, radar, and environmental observations into compact 32 dimensional embeddings. Their temporal evolution is then modeled by a causally masked Trans former that predicts multiple future latent states from pre ceding observations. Relative temporal order is represented using Rotary Position Embeddings, while month and year embeddings encode seasonal phase and interannual con text.

cs.CV↗

arXiv2026

CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV

In India, crop germination is primarily monitored by visual inspection and manual counting, which are prone to errors, despite their crucial role in determining eventual yield potential. This paper highlights a deep learning based pipeline which uses object detection methods and drone imagery to assess and provide a precise count of sugarcane germination in fields. The approch uses a pre-trained AI model to find germinated plant sampling and identify gaps, also known as ``bald spots'', which restricts field productivity. The techniques used here relies on the YOLOV8 architecture, which was trained on a carefully selected dataset of UAV photos taken in various agroclimatic zones of India. Here, we bring upon a novel orientation-normalization technique that uses minimum Spanning Trees (MST) to account for variations in planting geometry, allowing for dependable row and column extraction across a variety of field layouts. By converting detected seedlings into spatial point clouds, emergence gaps can be inferred from the anticipated spacing between plants. A geospatial germination map exported in Well-Known Text (WKT) format is the end result, and it can be easily incorporated into GIS platforms used by sugar mills and agronomists to direct transplant initiatives. Timely interventions based on the insights provided by the algorithm can significantly increase yield, resulting in higher profits. Hence, support proper allocation of resources, avoid wastage, and enhance long-term sustainability.

cs.CV↗

arXiv2026

STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series

Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), built on a self-supervised 3D-convolutional autoencoder (3D-CAE), designed to utilize Satellite Image Time Series (SITS) data for crop stress detection. STS-NET exploits four vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (GNDVI), Red-Edge Chlorophyll Index (RECI) and Normalized Difference Red-Edge Index (NDRE) obtained from high resolution Planetscope imagery to capture spatiotemporal stress patterns. The model is trained on our BSPT (Barnala Spatial-Temporal) dataset and evaluated on a real-world sugarcane dataset collected over a year from a 2.5-acre test plot located in Lakhimpur-Kheri (LK) district in Uttar Pradesh in India. STS-NET achieved a precision of 97. 98\% for water stress, 85.08\% for nitrogen stress, and 83.47\% for combined stress. The results demonstrate the potential of STS-NET in effectively detecting stress in sugarcane crops with minimal reliance on labeled data. Furthermore, STS-NET can serve as a robust feature extractor for simpler models.

cs.CV↗

arXiv2024

Evaluating Sugarcane Yield Variability with UAV-Derived Cane Height under Different Water and Nitrogen Conditions

This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nitrogen levels (low, medium, and high), with repeated treatments. In pixel distribution of DSM for each block, it provided bimodal distribution representing two peaks, ground level (gaps within canopies) and top of the canopies respectively. Using bimodal distribution, mean cane height was extracted for each block by applying a trimmed mean to the pixel distribution, focusing on the top canopy points. Similarly, the extracted mean elevation of the base was derived from the bottom points, representing ground level. The Derived Cane Height Model (DCHM) was generated by taking the difference between the mean canopy height and mean base elevation for each block. Yield measurements (tons/acre) were recorded post-harvest for each block. By aggregating the data into nine treatment zones (e.g., high water-low nitrogen, low water-high nitrogen), the DCHM and median yield were calculated for each zone. The regression analysis between the DCHM and corresponding yields for the different treatment zones yielded an R 2 of 0.95. This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data.

cs.CV↗