paper-with-me

홈 › Papers

Land use/land cover dynamics on vulnerable regions in Uruguay approached by a method combining Maximum Entropy and Population Dynamics

2022-12-13 · Johny Arteaga, Jhonny Agudelo, Alejandro Brazeiro, Hugo Fort

We present an exploratory population dynamics approach, described by Lotka-Volterra (LV) generalized equations, to explain/predict the dynamics and competition between land use/land cover (LULC) classes over vulnerable regions in Uruguay. We use the Mapbiomas-Pampa dataset composed by 20 annual LULC maps from 2000-2019. From these LULC maps we extract the main LULC classes, their spatial distribution and the time series of areas covered for each class. The interaction coefficients between species are inferred through the pairwise maximum entropy (PME) method from the spatial covariance matrices for different training periods. The main finding is that this LVPME method globally outperforms the more traditional Markov chains approach at predicting the trajectories of areas of LULC classes.

📄 PDF Abstract BibTeX arXiv:2212.06612

Code (0)

등록된 구현이 없습니다.

Tasks

Time SeriesTime Series Analysis

Similar Papers 제목 키워드 기반

The Canadian Cropland Dataset: A New Land Cover Dataset for Multitemporal Deep Learning Classification in Agriculture

2023-05-31 · Amanda A. Boatswain Jacques, Abdoulaye Baniré Diallo, Etienne Lord

Monitoring land cover using remote sensing is vital for studying environmental changes and ensuring global food security through crop yield forecasting. Specifically, multitemporal remote sensing imagery provides relevan…

Land Cover Classification

Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji

2025-09-16 · Yadvendra Gurjar, Ruoni Wan, Ehsan Farahbakhsh, Rohitash Chandra arxiv

As a developing country, Fiji is facing rapid urbanisation, which is visible in the massive development projects that include housing, roads, and civil works. In this study, we present machine learning and remote sensing…

Change Detection

Extracting Global Dynamics of Loss Landscape in Deep Learning Models

2021-06-14 · Mohammed Eslami, Hamed Eramian, Marcio Gameiro, William Kalies 외

Deep learning models evolve through training to learn the manifold in which the data exists to satisfy an objective. It is well known that evolution leads to different final states which produce inconsistent predictions …

Deep Learning

Noise-Driven Exploration and Transient Freezing Select Flat Minima in Stochastic Gradient Descent

2026-01-16 · Ning Yang, Yikuan Zhang, Qi Ouyang, Chao Tang 외 arxiv

Stochastic gradient descent (SGD) is central to deep learning, yet the dynamical origin of its preference for flatter, more generalizable solutions remains unclear. Here, by analyzing SGD learning dynamics, we identify a…

Tropical Land Use Land Cover Mapping in Pará (Brazil) using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data

2017-09-22 · Ron Hagensieker, Ribana Roscher, Johannes Rosentreter, Benjamin Jakimow 외

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high …