A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning models perform variably across continents/seasons in land cover mapping.
New method maps land cover using radar and optical satellite images.
Generative adversarial approach for satellite image time series land cover classification.
Sourcerer uses deep learning to map land cover from limited labeled data.
Bayesian framework improves ML classification models' uncertainty estimates.
Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality la…
Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in th…
This paper improves land cover classification using global spatial features in CNN.
JigsawHSI improves land-use classification using hyperspectral images.
Meta-learning improves few-shot land cover classification across diverse regions.
Study uses aerial or satellite imagery to improve land price prediction in Thailand.
There is a small number of case studies of automatic land cover classification on the coastal area. Here, I test extraction of seagrass beds, sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by comparing manual tracing, simple image segmentation, and image transformation using deep learning. The re…
Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although massive maps are available in the digital era, how to effectively and accurately access the required map remains a challenge today. Previo…
Deep learning models predict faster dune migration in arid regions.
The paper studies conditions for the non-existence of Cannon-Thurston maps in hyperbolic groups.
In this paper we present an analysis of power law statistics on land markets. There have been no other studies that have analyzed power law statistics on land markets up to now. We analyzed a database of the assessed value of land, which is officially monitored and made available to the public by the Ministry of Land, …
Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanning point clouds representing different classes of ground cover. The aim is to derive a supervised learning model for the classification usin…
Engine predicts real-time air quality with high resolution.
New metrics improve landing algorithms for orthogonality constraints.
This paper classifies typhoon damage features using aerial photography.
Model shows how relaxed leverage can lead to asset price bubbles.
The size distribution of land plots is a result of land allocation processes in the past. In the absence of regulation this is a Markov process leading an equilibrium described by a probabilistic equation used commonly in the insurance and financial mathematics. We support this claim by analyzing the distribution of tw…
Dataset for rainfall modeling in central Europe from 1981-2011.
We prove that affine invariant manifolds in strata of flat surfaces are algebraic varieties. The result is deduced from a generalization of a theorem of Möller. Namely, we prove that the image of a certain twisted Abel-Jacobi map lands in the torsion of a factor of the Jacobians. This statement can be viewed as a split…
Cryptocurrency and NFT prices are highly correlated, mirroring historical bubbles.
In this paper we propose the use of multiple local binary patterns(LBPs) to effectively classify land use images. We use the UC Merced 21 class land use image dataset. Task is challenging for classification as the dataset contains intra class variability and inter class similarities. Our proposed method of using multi-…
A hybrid model combines machine learning with a land surface model to improve soil moisture predictions.
In this paper we investigate quantitatively statistical properties of ensemble of {\it land prices} in Japan in the period from 1981 to 2002, corresponding to the period of bubbles and crashes. We find that the tail of the distributions of ensembles of the land prices in the high price range is well described by a powe…
Sandbox LAND prices differ based on unit of account, affecting investment returns.
First European crop map created using satellite data.
In "Rips complexes and covers in the uniform category" \cite{Rips} the authors define, following James \cite{J}, covering maps of uniform spaces and introduce the concept of generalized uniform covering maps. Conditions for the existence of universal uniform covering maps and generalized uniform covering maps are given…
We investigate the dynamical behavior in the large scale region of non-equilibrium systems, by employing data on the assessed value of land in 1983 -- 2006 Japan. In the system we find the detailed quasi-balance, which has the symmetry: x_1 -> a {x_2}^θ (x_1 and x_2 are two successive land prices). By using the detaile…
M. Khovanov and L. Rozansky gave a categorification of the HOMFLY-PT polynomial. This study is a generalization of the Khovanov-Rozansky homology. We define a homology associated to the quantum link invariant, where is the set of the fundamental representations of the quantum group of $sl…
GeoLifeCLEF 2020 dataset pairs species observations with environmental data.
The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric. We suggest to replace this metric with a locally adaptive, smoothly changing (Riemannian) metric that favors regions of high local density. The resulting locally adap…
Han discusses variants of digital covering maps and their equivalences.
Study of lifting maps in branched covers of 3-manifolds, showing non-injectivity.
Study models parking duration using machine learning and interpretable methods.
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classi…
The paper studies harmonic maps between surfaces homotopic to a covering map, proving uniqueness and injectivity of Hopf differential.
We study finite-sum nonconvex optimization problems, where the objective function is an average of nonconvex functions. We propose a new stochastic gradient descent algorithm based on nested variance reduction. Compared with conventional stochastic variance reduced gradient (SVRG) algorithm that uses two reference …
The paper studies liftable mapping class groups of cyclic covers of spheres.
Unified framework for constrained diffusion models on nonconvex sets with efficient landing mechanism.
Study models forest transitions with deep learning for parameter estimation.
Model for assembly map of bordism-invariant functors.
Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncer…
MapLUR uses deep learning on map images to estimate NO2 pollution, outperforming traditional methods.
This paper shows semi-equivelar toroidal maps are vertex-transitive covers.