Synthesized intelligence report assembled by querying open repositories for: “Spatial data analysis and GIS mapping”.
Extracted Findings
- [Bing News] Spatial Analysis Lab (Similarity: 0.603)
The Spatial Analysis Lab (the SAL) is a mapping lab dedicated to thoughtful and creative spatial data collection, analysis, and visualization. We strive to promote spatial literacy and spatial justice … - [Bing News] UC San Diego Students Learn Spatial Data Science Through Real-World Applications (Similarity: 0.4353)
Developed in response to the need for a new generation of spatial data scientists, the course bridges traditional geographic information systems (GIS) training with modern machine learning and data … - [Bing News] Agricultural Spatial Analysis and Modelling (Similarity: 0.4016)
Agricultural spatial analysis and modelling bring together geographic information systems (GIS), remote sensing, statistical techniques and machine-learning to understand and optimise farming across … - [Bing News] Mapping Tumor Terrain with Spatial Biology Tools (Similarity: 0.2491)
Spatial biology is reshaping how researchers study cancer by revealing the architecture and complexity of tumors in extraordinary detail. Through techniques that combine protein- and gene-level … - [arXiv] Data Encoding for Byzantine-Resilient Distributed Optimization (Similarity: 0.1845)
We study distributed optimization in the presence of Byzantine adversaries, where both data and computation are distributed among $m$ worker machines, $t$ of which may be corrupt. The compromised nodes may collaboratively and arbitrarily deviate from their pre-specified programs, and a designated (master) node iteratively computes the model/parameter vector for generalized linear models. In this work, we primarily focus on two iterative algorithms: Proximal Gradient Descent (PGD) and Coordinate Descent (CD). Gradient descent (GD) is a special case of these algorithms. PGD is typically used in the data-parallel setting, where data is partitioned across different samples, whereas, CD is used in the model-parallelism setting, where data is partitioned across the parameter space.
In this paper, we propose a method based on data encoding and error correction over real numbers to combat adversarial attacks. We can tolerate up to $tleq lfloorfrac{m-1}{2}rfloor$ corrupt worker nodes, which is information-theoretically optimal. We give deterministic guarantees, and our method does not assume any probability distribution on the data. We develop a {em sparse} encoding scheme which enables computationally efficient data encoding and decoding. We demonstrate a trade-off between the corruption threshold and the resource requirements (storage, computational, and communication complexity). As an example, for $tleqfrac{m}{3}$, our scheme incurs only a {em constant} overhead on these resources, over that required by the plain distributed PGD/CD algorithms which provide no adversarial protection. To the best of our knowledge, ours is the first paper that makes CD secure against adversarial attacks.
Our encoding scheme extends efficiently to the data streaming model and for stochastic gradient descent (SGD). We also give experimental results to show the efficacy of our proposed schemes. - [Bing News] The rise of artificial intelligence meets the golden age of geography (Similarity: 0.1606)
Millions of maps are made each day, specifically data-rich maps that guide predictions and decisions for the world’s largest organisations. This spatial analysis – a time-tested technology – mixes the … - [arXiv] TerraGen: A Unified Multi-Task Layout Generation Framework for Remote Sensing Data Augmentation (Similarity: 0.14)
Remote sensing vision tasks require extensive labeled data across multiple, interconnected domains. However, current generative data augmentation frameworks are task-isolated, i.e., each vision task requires training an independent generative model, and ignores the modeling of geographical information and spatial constraints. To address these issues, we propose textbf{TerraGen}, a unified layout-to-image generation framework that enables flexible, spatially controllable synthesis of remote sensing imagery for various high-level vision tasks, e.g., detection, segmentation, and extraction. Specifically, TerraGen introduces a geographic-spatial layout encoder that unifies bounding box and segmentation mask inputs, combined with a multi-scale injection scheme and mask-weighted loss to explicitly encode spatial constraints, from global structures to fine details. Also, we construct the first large-scale multi-task remote sensing layout generation dataset containing 45k images and establish a standardized evaluation protocol for this task. Experimental results show that our TerraGen can achieve the best generation image quality across diverse tasks. Additionally, TerraGen can be used as a universal data-augmentation generator, enhancing downstream task performance significantly and demonstrating robust cross-task generalisation in both full-data and few-shot scenarios. - [PubMed] Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping. (Similarity: 0.1252)
Source: Sci Rep. Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping. - [PubMed] Spatiotemporal Modeling of Premature Mortality in Mississippi: A County-Level Analysis of Years of Potential Life Lost, 2015-2025. (Similarity: 0.124)
Source: Prev Chronic Dis. Spatiotemporal Modeling of Premature Mortality in Mississippi: A County-Level Analysis of Years of Potential Life Lost, 2015-2025. - [PubMed] Secondary multifractal analysis for precise background value extraction and heavy metal pollution assessment in Shantou soils. (Similarity: 0.1195)
Source: Environ Geochem Health. Secondary multifractal analysis for precise background value extraction and heavy metal pollution assessment in Shantou soils. - [arXiv] Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data (Similarity: 0.1158)
We study distributed stochastic gradient descent (SGD) in the master-worker architecture under Byzantine attacks. We consider the heterogeneous data model, where different workers may have different local datasets, and we do not make any probabilistic assumptions on data generation. At the core of our algorithm, we use the polynomial-time outlier-filtering procedure for robust mean estimation proposed by Steinhardt et al. (ITCS 2018) to filter-out corrupt gradients. In order to be able to apply their filtering procedure in our {em heterogeneous} data setting where workers compute {em stochastic} gradients, we derive a new matrix concentration result, which may be of independent interest.
We provide convergence analyses for smooth strongly-convex and non-convex objectives. We derive our results under the bounded variance assumption on local stochastic gradients and a {em deterministic} condition on datasets, namely, gradient dissimilarity; and for both these quantities, we provide concrete bounds in the statistical heterogeneous data model. We give a trade-off between the mini-batch size for stochastic gradients and the approximation error. Our algorithm can tolerate up to $frac{1}{4}$ fraction Byzantine workers. It can find approximate optimal parameters in the strongly-convex setting exponentially fast and reach to an approximate stationary point in the non-convex setting with a linear speed, thus, matching the convergence rates of vanilla SGD in the Byzantine-free setting.
We also propose and analyze a Byzantine-resilient SGD algorithm with gradient compression, where workers send $k$ random coordinates of their gradients. Under mild conditions, we show a $frac{d}{k}$-factor saving in communication bits as well as decoding complexity over our compression-free algorithm without affecting its convergence rate (order-wise) and the approximation error. - [PubMed] Mapping Drought Vulnerability in the Chi River Basin, Thailand: A Machine Learning Framework Using H3 Hexagonal Grids and Topographic Variables. (Similarity: 0.1127)
Source: Sensors (Basel). Mapping Drought Vulnerability in the Chi River Basin, Thailand: A Machine Learning Framework Using H3 Hexagonal Grids and Topographic Variables.