Predicting Landslides Using Contour Aligning Convolutional Neural Networks

Ainaz Hajimoradlou1 , Gioachino Roberti2 , David Poole11University of British Columbia

2Minerva Intelligence{ainaz, poole}@cs.ubc.ca, groberti@minervaintelligence.com

Abstract

Landslides, movement of soil and rock under theinfluence of gravity, are common phenomena thatcause significant human and economic losses ev-ery year. Experts use heterogeneous features suchas slope, elevation, land cover, lithology, rock age,and rock family to predict landslides. To work withsuch features, we adapted convolutional neural net-works to consider relative spatial information forthe prediction task. Traditional filters in these net-works either have a fixed orientation or are rotation-ally invariant. Intuitively, the filters should orientuphill, but there is not enough data to learn the con-cept of uphill; instead, it can be provided as priorknowledge. We propose a model called LocallyAligned Convolutional Neural Network, LACNN,that follows the ground surface at multiple scalesto predict possible landslide occurrence for a sin-gle point. To validate our method, we created astandardized dataset of georeferenced images con-sisting of the heterogeneous features as inputs, andcompared our method to several baselines, includ-ing linear regression, a neural network, and a con-volutional network, using log-likelihood error andReceiver Operating Characteristic curves on thetest set. We show that our model performs betterthan the other proposed baselines.

1 IntroductionLandslides, the downslope movement of Earth materials un-der the influence of gravity, are common and destructive phe-nomena. Despite the number of studies focusing on land-slide mapping [Guzzetti et al., 2012] and landslide spatial andtemporal probability prediction [Reichenbach et al., 2018;Baron and Ottowitz, 2012], effective real-world applicationsare scarce and landslides cause significant life and economiclosses every year [Petley, 2012]. There are three differ-ent approaches to landslide susceptibility mapping: expert-based, physical-based, and statistical approaches. Expert-based methods rely on the qualitative judgment of a domainexpert, while physical-based approaches model the stabilityof a slope given physical parameters such as geotechnical

rock and soil properties, and calculate the equilibrium be-tween destabilizing factors and slope strength, but often re-quire more information than is available at scale. Statisti-cal models rely on the statistical analysis of large landslidedatabases and their relation with landscape attributes. Land-scape attributes typically include internal (e.g. slope angle,rock type, etc.) and external (e.g. rainfall) properties of theslope. These data are then used to map the spatial and/or tem-poral probability of slope failure [Baron and Ottowitz, 2012].The spatial probability of landslide occurrence is usually re-ferred to as a susceptibility map. When the magnitude andthe temporal component (e.g. frequency and triggers) are alsoconsidered, it is referred to as a hazard map [Baron and Ot-towitz, 2012].

Statistical approaches for predicting landslides have sig-nificantly increased in recent years. However, they mostlyapply models such as linear logistic regression, Support Vec-tor Machines (SVM), or neural networks [Reichenbach et al.,2018]. In this study, we propose a novel convolutional modelwhich we call a Locally Aligned Convolutional Neural Net-work, LACNN, for producing susceptibility maps. Convo-lutional Neural Networks, CNNs, form a category of neuralnetwork models with tied parameters [LeCun et al., 1999].CNNs with pooling layers can capture both local and globalfeatures of an image, which has been proven extremely usefulin many vision tasks such as object recognition, image clas-sification, and object detection.

We are interested in predicting the landslide probabilityfor each point on the ground. The output of our model is aprobability map with the same resolution as the input fea-tures. We use a fully convolutional model [Shelhamer etal., 2017] for this purpose. These models have been widelyused for image segmentation [Ronneberger et al., 2015;Noh et al., 2015] and usually consist of down-sampling andup-sampling stages. One of the popular models in this cate-gory is UNet [Ronneberger et al., 2015], which our architec-ture is based on. The down-sampling stage consists of convo-lutions with pooling layers and tries to create a set of compactfeatures capturing both local and global properties of the in-put features. The up-sampling stage typically consists of con-volution transpose layers which are mainly doing the inverseof pooling but with learned parameters. We do not use con-volution transpose layers in our model as they tended to pro-duce checkboard artifacts in our experiments, which is a com-

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mon problem in the literature as well [Aitken et al., 2017].Instead, we use interpolation for up-sampling. It has beenshown that adding skip connections to a fully convolutionalmodel improves its performance [Drozdzal and others, 2016;Mao et al., 2016]. As short skip connections have been shownto work only in very deep networks, we only apply long con-nections to our model.

To produce good susceptibility maps for landslides, weproposed learning filters that can follow the ground surfaceand extract features towards the uphill direction. For thisto work, we need the CNN model to preserve orientationalinformation of landslides to each other but this is not pos-sible using traditional techniques, when the filters are ei-ther rotationally invariant or align themselves up, down, left,and right, which corresponds to north, south, east, and west.Capsul networks [Sabour et al., 2017; Ahmad et al., 2018;Ramasinghe et al., 2018] have been recently proposed to ad-dress this issue however, they are not suitable for the task oflandslide prediction. We add a pre-processing stage to ourCNN model to find the best directions for each pixel at mul-tiple scales and then learn hidden features according to thosedirections. We call this model a Locally Aligned CNN as themodel first aligns itself to a specific set of orientations andthen learns a classifier.

The contributions of our paper are:

• We provide a standardized dataset so that others cancompare their results to ours. This dataset is compiledfrom public domain data from various sources, includ-ing the CORINE land cover inventory1, Italian NationalGeoportal website2, and the National Institute of Geo-physics and Volcanology3. The dataset consists of sev-eral input features such as the slope, elevation, rocktypes with age and family, and land cover, along with theground truth in the shape of landslide polygons whichcan be used in both a supervised and unsupervised learn-ing framework.

• We propose a novel statistical approach for predictinglandslides using deep convolutional networks. We de-velop a model that can capture each pixel’s orientation atmultiple different ranges to classify a landslide. We useranges of 30, 100, and 300 meters in our model. Thesescales can be optimized using cross-validation.

• We define several baseline models for comparison. Weprovide five different baselines including a Naive model,a linear logistic regression (LLR), a neural network(NN), and a locally aligned neural network (LANN)model without any convolutions to compare our model’sperformance against them (which can also be seen as ab-lation studies for our model).

• We provide a way to use CNN models with heteroge-neous datasets for predicting landslides rather than onlyusing images in our models.

1https://land.copernicus.eu/pan-european/corine-land-cover2http://www.pcn.minambiente.it/mattm/en/wfs-service/3http://tinitaly.pi.ingv.it/

2 Related WorkProducing susceptibility maps by statistical approaches isnot new in the landslide community. Many people havebeen using models such as logistic regression, SVM, andrandom forests. Catani et al. [2013] used random foreststo generate susceptibility maps emphasizing on sensitivityand scaling issues. Micheletti et al. [2013] and Youssefet al. [2014] also used random forest models in predict-ing landslides for Switzerland and Wadi Tayyah Basin inSaudi Arabia. Some have developed software packages us-ing random forests for susceptibility mapping [Behnia andBlais-Stevens, 2017]. Micheletti et al. [2013] generate sev-eral susceptibility mappings using SVMs, random forests,and Adaboost. Atkinson and Massari [1998], Ayalew andYamagishi [2005], and Davis et al. [2006] focus on lin-ear regression for predicting landslides due to its simplic-ity and easy training procedure. There is a volume of ap-proaches that formulate the problem in a probabilistic frame-work such as Bayesian networks [Heckmann et al., 2015;Lombardo et al., 2018].

Neural networks and convolutional models are amongmore recent approaches for susceptibility mapping. Luo etal. [2019] and Bui et al. [2015] use neural networks to assessmine landslide susceptibility and to predict shallow landslidehazards. Wang et al. [2019] did a comparative study on CNNsfor landslide susceptibility mapping but their approach doesnot incorporate any orientational information or aligning fil-ters either. The existing convolutional models are not usuallydeep and do not use any pooling layers to consider multi-ple resolutions for feature extraction [Xiao L, 2018]. Mostof these models used in landslide susceptibility mapping arequite simple and do not take into account any orientations.Additionally, their networks do not contain filters that can ro-tate or capture orientation between landslides. Our proposedCNN architecture is much more sophisticated; it is a fullyconvolutional network that down-samples images at multipleresolutions and learns filters that can align themselves to theuphill direction. Moreover, our model is trained on geospatialdata rather than satellite images.

3 DatasetThe dataset used for predicting landslides is from Italianopen-source databases. The dataset contains both continuousand categorical features in the shape of rasters and vector filesrespectively. Continuous features including slope and DEM4

contain out of range values while categorical features such asrock type, land cover, rock age, and rock family, have severalno-data points. To use such data in a CNN, we converted eachvector map to a raster after removing invalid data points andout of range values.

As we wanted to propose a baseline framework for thistype of problem, we needed to come up with a standard set offeatures for our categorical data. We chose 44 rock types, 5land covers, 5 rock families, and 38 rock ages, based on theINSPIRE terminology, as the one-hot encoding for our cat-

4Digital Elevation Model

egorical data. INSPIRE5 is a European Union directive forstandardizing spatial data across countries in Europe.

Using the INSPIRE terminology, we ended up with 94standard input features. These features include 44 lithologyor rock type features (such as gneiss, mica-schist, granite, andsiltstone, etc.), 5 land cover features (agricultural areas, artifi-cial surfaces, forest and semi-natural areas, water bodies, andwetlands), 4 rock family features (metamorphic, sedimentary,plutonic, and volcanic), 38 rock age features (such as paleo-zoic cycle, cretaceous-jurassic cycle, and average triassic cy-cle, etc.), and digital elevation model maps, resulting in 92features. We also use an unknown class for the rock familyfeatures along with a slope map. This results in 94 features intotal.

We chose Veneto, a region of Italy, since it expands overboth mountains and flat zones close to the sea. Each pixelin our prepared dataset has a 10 meters resolution and theimages are 21005×19500 pixels resulting in an area with ap-proximately 210 (km) width and 195 (km) height. The ra-tio of landslides in this region is below 1% which makes thedataset extremely imbalanced. The landslides in Veneto in-clude both mountainous and less steep areas which are goodfor training our model. Unfortunately, the landslides do notusually contain information about the date of occurrence. Allof these characteristics make this dataset challenging from themachine learning point of view. We will make this datasetavailable for other researchers.

4 Locally Aligned Convolutional NeuralNetwork

The slope is considered one of the main conditioning factorsin predicting landslides. The LLR baseline that we learnedalso confirms this claim as the slope’s weight is among thetop 5 learned weights. Traditional CNN filters are orientedvertically in an image, but the important orientation is uphilland downhill for landslides. Based on this, we propose a Lo-cally Aligned CNN model with filters that align themselvesaccording to the uphill direction and extract features along-side that direction. For each pixel, we look at three differentranges and choose the highest elevation value at each range,and extract relevant features at those points (refer to Figure1). Because space is at a premium for batch size, we selecteda subset of 22 features for this purpose. These features arechosen based on our trained LLR baseline. We chose a fea-ture if the absolute value of its logistic regression weight is0.2 or greater.

4.1 ArchitectureOur Locally Aligned CNN architecture consists of a prepro-cessing module and four layers of down-sampling and up-sampling as in Figure 2. The preprocessing module takesthe elevation map along with other input features from thedataset as inputs and outputs 22 aligned features for eachlooking distance. We use 30, 100, and 300 meters as look-ing distances in our experiments but it can also be considered

5Infrastructure for Spatial Information in Europe: https://inspire.ec.europa.euhttps://inspire.ec.europa.eu

Figure 1: The process of finding aligned features at multiple scales.The red point shows the point of interest where we want to findthe uphill directions. Each blue circle shows a set of neighboringpoints at a specific range. The green point in each circle is the de-tected point with the highest elevation at that distance, from whichthe aligned features will be extracted.

a hyper-parameter and be optimized using cross-validation.The preprocessing module outputs 66 aligned features thatwe further feed into the convolutional network along with theoriginal 94 features. We apply long skip connections betweeneach sampling layer in our LACNN architecture. Each down-sampling layer consists of two convolution layers followedby Relu as non-linearity and a max-pooling layer. Every up-sampling layer includes an up-sampling module to interpolatethe data followed by convolutions and Relu. In the end, weapply a Sigmoid function to the output of the model to obtainprobabilities.

4.2 TrainingThe rasters in the dataset are too large to fit into a 12 GBmemory of a TitanXP GPU when training. Instead, we di-vide each raster, an input feature, into smaller images of size500×500, which we call patches. We further feed mini-batches of these patches into our model for training. Sincewe want to produce a coherent probability map for the wholeregion, we use patches that overlap each other. For this pur-pose, we pad each patch with 64 pixels on each side resultingin 628×628 images. This padding number is used to ensurethat the overlap between patches is bigger than the receptivefield of view of our networks. We partitioned these patchesinto training, testing, and validation sets.

We randomly partitioned image patches such that 80% ofthe data is used for training, 10% for testing, and the other10% for validation. We use the negative log-likelihood lossto train our model. However, as the training data is extremelyimbalanced, we use oversampling to balance the data to someextent. Since we want to train our model on patches andpreserve the spatial relation between pixels, we oversamplepatches that have at least one positive label. By oversamplingthose patches, we are oversampling both landslides and non-landslide pixel points. After doing this, the distribution oflandslides stays below 1%. This oversampling technique canalso be seen as a type of data augmentation which providesmore training data. We used an oversampling ratio of 5 in ourexperiments.

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4.3 BaselinesWe propose a baseline model called Naive that predicts 0.013(ratio of landslides in the training set) everywhere in the im-age. Given the ratio of negative/zero labels, and the ratio ofpositive/one labels, we can calculate the expected negativelog-likelihood error of the Naive baseline on train and testsets. This is approximately equal to 0.069 for the train setand 0.065 for the test set. We compare the test and trainingerrors of our other baselines with the Naive model to makesure that the learned models perform better than Naive (referto Table 2).

Our model, LACNN, has two main characteristics: convo-lutions and feature extraction from the uphill direction. Toshow the effect of each of these characteristics, we proposea baseline (CNN) that does not pay attention to the uphill di-rection and only uses convolutions to predict landslides andanother baseline (LANN) that does not use any convolutionsbut extracts features from the uphill direction. We also pro-pose NN, which is a simple neural network that neither usesconvolutions nor uphill features, and a linear logistic regres-sion model (LLR) to compare our results to. These modelscan be looked at as ablation studies to our proposed model(LACNN), which show that locally aligned filters in a convo-lutional framework are the most effective of all.

4.4 Hyper-ParametersTable 1 shows the hyper-parameters used for training each ofthese models. We optimized the learning rate and the opti-mizer with 5-fold cross-validation for one epoch. The batchsize is chosen such that we can fit the maximum number ofpatches in the memory. The number of epochs is chosen tofully train each model. We validate our models at each epochand reduce the learning rate if the validation error keeps in-creasing for patience number of epochs to avoid overfitting.We chose patience = 2 and decay = 0.001, which is theL2 regularization lambda, in our experiments. The code isavailable publicly under https://anonymous.4open.science/r/0d078532-bf00-40c7-b3cd-1d51dab99d03/.

Table 1: Trained Hyper-Parameters. LR and BS represent the learn-ing rate and the batch size respectively.

MODEL OPTIMIZER LR EPOCHS BSLLR Adam 0.125 10 15NN Adam 0.125 10 13LANN Adam 0.0156 15 10CNN SGD 0.125 20 12LACNN Adam 0.001 30 9

5 ResultsWe show the final susceptibility map of our main model,LACNN, with the corresponding ground truth in Figures 5aand 5b. This susceptibility map is for the whole region ofVeneto. The produced probability map contains many de-tails and can identify areas with high susceptibility around thelandslides. Since time scale is not provided for landslides, theoutput probabilities are for an undefined period, and thereforeshould only be interpreted as relative scales. We also illustratethe number of patches that were used for training, validation,and testing in the whole region in Figure 3.

Figures 5c-5g show the results of all models for a smallerregion of Veneto so that we can compare the output of variousmodels against each other with more details since the origi-nal susceptibility map is too large. This region includes bothlandslide polygons and non-landslide areas and has a varietyof terrain. Figures 5c-5g show that the susceptibility map be-comes more detailed as the number of parameters increasesand the model gets more complicated. The range of the pre-dicted probabilities is also different between models. Morecomplicated models have a larger variance between their pre-dictions.

5.1 Receiver Operating Characteristic CurvesWe evaluate our model, LACNN, against other baselines us-ing the Receiver Operating Characteristic (ROC) curve on the

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Figure 3: This image shows the data partitioning used for the wholeregion of Veneto. Grey, blue, and green colors are used to representtrain, validation, and test sets respectively. The light background isoutside of the region.

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Figure 4: ROC curves from all models on the test set.

Table 2: Negative Log Likelihood Loss

METHOD TEST ERR TRAIN ERR AUCNaive 0.065 0.069 0.50LLR 0.055 0.057 0.80NN 0.052 0.055 0.83LANN 0.048 0.052 0.85CNN 0.047 0.051 0.85LACNN 0.046 0.050 0.87

test set and show that it achieves the best results among allmodels, as shown in Figure 4. Note that the LANN modelachieves similar performance to the CNN model. This is in-teresting since this model does not use any convolutions forpredictions and only looks at the uphill direction at three dif-ferent distances (30, 100, and 300 meters), suggesting thatalignment plays a significant role in predicting landslides.

5.2 Negative Log-Likelihood ErrorWe further assess our model by computing the negative log-likelihood error on both the training and the test sets. Table2 illustrates the results of all baselines. The LACNN modelobtains the lowest errors on both train and test sets.

6 ConclusionLandslides are the movement of ground under the force ofgravity. They are common phenomena that can cause signifi-cant casualties. There have been many approaches to producesusceptibility maps to reduce the impact of landslides includ-ing expert-based, physics-based, and statistical methods. Allof these methods have their flaws and lack a standard set offeatures. We provide a standardized open-source dataset withthe same terminology as INSPIRE so that anyone who usesthe INSPIRE terminology can compare their results to ourproposed baselines. We also propose a novel statistical ap-proach for predicting landslides using machine learning. Weintroduce a deep convolutional model, called LACNN, thatcan follow the ground surface and align itself with the groundcontour lines to extract relevant features. We evaluate ourmodel by ROC curves and negative log-likelihood error andshow that it can achieve the best results on the test set amongall the baselines. Our results suggest that this type of statis-tical approach is effective for generating susceptibility mapswhich in turn has the potential to alleviate human and finan-cial losses caused by landslides.

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(a) Probability map of whole Veneto.

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Figure 5: Figures 5a and 5b show the probability map given by LACNN model and its corresponding ground truth of the whole region with21005×19500 resolution: red regions correspond to higher probabilities and blue regions are areas with probabilities close to zero and thered polygons represent observed landslides in the region. Figures 5c-5g represent the acquired probability maps from different models for asmaller region in Veneto with 2500×2500 resolution and Figure 5h is the corresponding ground truth.

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