Landslide Segmentation with U-Net: Evaluating Different Sampling Methods
and Patch Sizes
- URL: http://arxiv.org/abs/2007.06672v1
- Date: Mon, 13 Jul 2020 20:28:46 GMT
- Title: Landslide Segmentation with U-Net: Evaluating Different Sampling Methods
and Patch Sizes
- Authors: Lucas P. Soares, Helen C. Dias, Carlos H. Grohmann
- Abstract summary: This study used a fully convolutional deep learning model named U-net to automatically segment landslides in the city of Nova Friburgo, Brazil.
The objective was to evaluate the impact of patch sizes, sampling methods, and datasets on the overall accuracy of the models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Landslide inventory maps are crucial to validate predictive landslide models;
however, since most mapping methods rely on visual interpretation or expert
knowledge, detailed inventory maps are still lacking. This study used a fully
convolutional deep learning model named U-net to automatically segment
landslides in the city of Nova Friburgo, located in the mountainous range of
Rio de Janeiro, southeastern Brazil. The objective was to evaluate the impact
of patch sizes, sampling methods, and datasets on the overall accuracy of the
models. The training data used the optical information from RapidEye satellite,
and a digital elevation model (DEM) derived from the L-band sensor of the ALOS
satellite. The data was sampled using random and regular grid methods and
patched in three sizes (32x32, 64x64, and 128x128 pixels). The models were
evaluated on two areas with precision, recall, f1-score, and mean intersect
over union (mIoU) metrics. The results show that the models trained with 32x32
tiles tend to have higher recall values due to higher true positive rates;
however, they misclassify more background areas as landslides (false
positives). Models trained with 128x128 tiles usually achieve higher precision
values because they make less false positive errors. In both test areas, DEM
and augmentation increased the accuracy of the models. Random sampling helped
in model generalization. Models trained with 128x128 random tiles from the data
that used the RapidEye image, DEM information, and augmentation achieved the
highest f1-score, 0.55 in test area one, and 0.58 in test area two. The results
achieved in this study are comparable to other fully convolutional models found
in the literature, increasing the knowledge in the area.
Related papers
- Less is More: Fewer Interpretable Region via Submodular Subset Selection [58.01691615408149]
This paper re-models the above image attribution problem as a submodular subset selection problem.
We construct a novel submodular function to discover more accurate small interpretation regions.
For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution.
arXiv Detail & Related papers (2024-02-14T13:30:02Z) - Country-Scale Cropland Mapping in Data-Scarce Settings Using Deep
Learning: A Case Study of Nigeria [0.6827423171182154]
We combine a global cropland dataset and a hand-labeled dataset to train machine learning models for generating a new cropland map for Nigeria in 2020 at 10 m resolution.
We provide the models with pixel-wise time series input data from remote sensing sources such as Sentinel-1 and 2, ERA5 climate data, and DEM data, in addition to binary labels indicating cropland presence.
We find that the existing WorldCover map performs the best with an F1-score of 0.825 and accuracy of 0.870 on the test set, followed by a single-headed LSTM model trained with our hand-labeled training
arXiv Detail & Related papers (2023-12-18T01:23:22Z) - Exploring the Effectiveness of Dataset Synthesis: An application of
Apple Detection in Orchards [68.95806641664713]
We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection.
We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset.
Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
arXiv Detail & Related papers (2023-06-20T09:46:01Z) - Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal
Segmentation [0.9176056742068814]
We propose a novel semi-supervised learning (SSL) approach that requires only a relatively small number of annotations.
Our method uses a pseudo-labeling technique that employs recent deep learning uncertainty estimation models.
Our proposed model outperformed the semi-supervised model in experiments with the ProstateX dataset and an external test set.
arXiv Detail & Related papers (2023-05-10T08:50:04Z) - Pre-processing training data improves accuracy and generalisability of
convolutional neural network based landscape semantic segmentation [2.8747398859585376]
We trialled different methods of data preparation for CNN training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia.
This was conducted through trialling and ranking various training patch selection sampling strategies, patch and batch sizes and data augmentations and scaling.
We fully trained five models on the 2018 training image and applied the model to the 2015 test image with the output LULC classifications achieving an average of 0.84 user accuracy of 0.81 and producer accuracy of 0.87.
arXiv Detail & Related papers (2023-04-28T04:38:45Z) - Contrastive Neighborhood Alignment [81.65103777329874]
We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features.
The target model aims to mimic the local structure of the source representation space using a contrastive loss.
CNA is illustrated in three scenarios: manifold learning, where the model maintains the local topology of the original data in a dimension-reduced space; model distillation, where a small student model is trained to mimic a larger teacher; and legacy model update, where an older model is replaced by a more powerful one.
arXiv Detail & Related papers (2022-01-06T04:58:31Z) - A contextual analysis of multi-layer perceptron models in classifying
hand-written digits and letters: limited resources [0.0]
We extensively test an end-to-end vanilla neural network (MLP) approach in pure numpy without any pre-processing or feature extraction done beforehand.
We show that basic data mining operations can significantly improve the performance of the models in terms of computational time.
arXiv Detail & Related papers (2021-07-05T04:30:37Z) - ALT-MAS: A Data-Efficient Framework for Active Testing of Machine
Learning Algorithms [58.684954492439424]
We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data.
The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
arXiv Detail & Related papers (2021-04-11T12:14:04Z) - Automated Model Design and Benchmarking of 3D Deep Learning Models for
COVID-19 Detection with Chest CT Scans [72.04652116817238]
We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification.
We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
arXiv Detail & Related papers (2021-01-14T03:45:01Z) - Dataset Cartography: Mapping and Diagnosing Datasets with Training
Dynamics [118.75207687144817]
We introduce Data Maps, a model-based tool to characterize and diagnose datasets.
We leverage a largely ignored source of information: the behavior of the model on individual instances during training.
Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
arXiv Detail & Related papers (2020-09-22T20:19:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.