This technical report describes the model that achieved the best performance of the GeoLifeCLEF challenge, the objective of which was to evaluate methods for plant species prediction based on their geographical location. Our method is based on an adaptation of the Inception v3 architecture initially dedicated to the classi cation of RGB images. We modi ed the input layer of this architecture so as to process the spatialized environmental tensors as images with 77 distinct channels. Using this architecture, we did train several models that mainly di ered in the used training data and in the predicted output classes. One of the main objective, in particular, was to compare the performance of a model trained with plant occurrences only to that obtained with a model trained on all available occurrences, including the species of other kingdoms. Our results show that the global model performs consistently better than the plant-speci c model. This suggests that the convolutional neural network is able to capture some inter-dependencies among all species and that this information signi cantly improves the generalisation capacity of the model for any species.
Predicting a list of the most likely species present at a given location can be very useful. First, it could improve species identi cation processes and tools by reducing the list of candidate species observable at a given location (whether automated, semi-automated or based on classical eld guides or ora). More generally, it could facilitate biodiversity inventories through the development of location-based recommendation services (typically on mobile phones) as well as the involvement of non-expert nature observers. Finally, it could be used for educational purposes through biodiversity discovery applications providing innovative features such as contextualized educational pathways.
This challenge is related to the problem of Species Distribution
Modelling (SDM) in ecology. SDM goal is to predict the spatial distribution of a
species over a territory [
Concretely, the objective of SDM is to infer the spatial distribution of a given species, and they are often based on a set of geolocalized occurrences of that species (collected by naturalists, eld ecologists, nature observers, citizen sciences project, etc.).
Recently, SDM based on deep neural networks have begun to appear [
In this paper, we present a study to evaluate a convolutional neural network to determine the ecological preferences of species through ranges of environmental image patches provided as input (temperature, soil type, etc.) as part of the GeoLifeCLEF challenge.
{ Section 2 gives an overview of the various data we used to build our model. { Section 3 provides the detailed description of our model.
{ Section 4 presents the results of the experiments and their analysis. 2 2.1
Occurrences
The data set protocol is explained in the challenge protocol [
{ plantnet data set: composed of 2,367,145 plant species occurrences with uncertain identi cations, because they come from the automatic identi cation of plant images, from the Pl@ntnet application. { glc 18: Global Biodiversity Information Facility (GBIF) data set (same data set as last year challenge), it is composed of 291,392 occurrences of 3,336 plant species observed on French territory between 1835 and 2017. { trusted data set: a sample of plantnet data set without uncertainty. { no plant: 10,618,839 species occurrences from the GBIF database, of other kingdoms (such as mammals, birds, amphibians, insects, fungus etc.). We have removed occurrences that didn't match any environmental rasters, for example birds in the middle of the sea.
Then, we lter the database to get only plant species that were given by the challenge.
We used di erent methods to structure our train data set from each data set. { full noplant: we train our model with all the data (plantnet, glc 18 and no plant) we had including animal observations and species from other kingdoms. { full prediction: we only used the Pl@ntnet data set and glc 18 data set. { lter predictions: we try a random weighted selection scheme, the same as the one explained in the challenge notes for the test set. For each occurrence si in our data set, we compute a weight wi which corresponds to the importance of this plant over an area of 2 kilometres. Then we did a random sampling on the zi number between 0 and the maximum weight, and we kept the occurrence only if zi < wi. We did this sampling over 2 data set: the plantnet data set with uncertain identi cation set at 70%, we had a train set around 32,000 occurrences after sampling, and another with the data set pl trusted, we had around 27,000 occurrences after sampling. 2.2
Environmental rasters
We have associated 33 rasters windows cropped into each corresponding global
environmental rasters provided by the challenge as input features to each
occurrence.[
These environmental rasters were constructed from various open data sets including Chelsea Climate, ESDB soil pedological data, Corine Land Cover 2012 soil occupation data, CGIAR-CSI evapotranspiration data, USGS Elevation data (Data available from the U.S. Geological Survey.) and BD Carthage hydrologic data. All these data try to best represent the environment where the plant is observed. We print them on a given area in the Figure 1, we can see the diversity of information we have for one occurrence near Montpellier.
In the following, we generally denote to x X an occurrence, each x being associated to a spatial position p(x) in the spatial domain D, a species label y(x) and an environmental tensor g(x) of size 64x64x33. We denote as P the set of all spatial positions p covered by X. It is important to note that a given spatial position p0 2 P usually corresponds to several occurrences xj 2 X, p(xj ) = p0 observed at that location. In the training set, up to several hundreds of occurrences can be located in the same place.
The environmental data provided [
It has been previously shown in [
Our model attempts to predict the most likely species to be observed based
on environmental features learned. Our structure is very di erent from last year
challenge [
For the last layer we used a softmax and a cross entropy loss. The softmax
layer is computed as:
where yk are the scores inferred by the net for each class, and K the number of
di erent classes that means the number of species we trained our model with,
the softmax activation for a class yj depends on all the scores in y.
Then we used a cross entropy loss [
ehj PK
k=1 ehk Loss =
K X yk log(pk) k=1 with yk a binary indicator if class label k is the correct classi cation for the observation and pk the probability of the observation is of class k.
Learning set up and parameters: all our experiments were conducted using PyTorch deep learning framework 4 and were run on a single computing node equipped with 4 Nvidia GTX 1080 ti GPU. We used the Stochastic Gradient Descent optimization algorithm with a learning rate of 0.1, a momentum of 0.9 and a batch size of 300.
We trained our model over multiple epochs, then to export our result we took the epoch where the model gets the highest Top 1 score in our validation set. For our best runs (full noplant predictions and full predictions) we trained only over 20 epochs due to the number of data and for the two other runs we trained our model over 180 epochs. 4
For GeoLifeCLEF 2019 they decided that the main evaluation criteria will be the accuracy based on the rst 30 answers, also called Top 30, i.e. the function scoring 1 when the right specie is in the 30 rst answers, and 0 if not. And as a (1) (2) Rank runId top30 runName participant 1 27007 0.1769 full noplant predictions LIRMM 2 27086 0.1687 RUN2 GRINNELL FULL INITIAL SaraSi 3 27087 0.1653 RUN3 GRINNELL TRANS SaraSi 4 27088 0.1646 RUN1 GRINNELL TESTSPECIES INITIAL SaraSi 5 27006 0.1364 full predictions LIRMM 6 26997 0.1342 submit xgb spatial 4x4 all SSN CSE 7 26996 0.1288 submit xgb spatial allnoclc SSN CSE 8 27013 0.1273 submit xgb dep3 1 SSN CSE 9 27069 0.1268 submission sel 4x4 SSN CSE 10 27012 0.1263 submit xgb 4x4 all dep3 SSN CSE 11 27070 0.1227 submission sel 1 SSN CSE 12 27064 0.1198 submission1x1 SSN CSE 13 27067 0.1135 submission4x4 SSN CSE 14 27124 0.1135 Lot Of Lof 2 Lot of Lof 15 27089 0.1110 RUN4 ELTON TRANS SaraSi 16 27082 0.1090 RUN0 ELTON FULL INITIAL TESTSPECIES SaraSi 17 26988 0.1063 submit xgb spatial SSN CSE 18 27123 0.0984 Lot Of Lof 3 Lot of Lof 19 27063 0.0864 Lot Of Lof 1 Lot of Lof 20 26875 0.0844 submission SSN CSE 21 27102 0.0834 rfspatial SSN CSE 22 26821 0.0570 submit SSN CSE 23 27004 0.0470 plcomplete predictions LIRMM 24 27005 0.0465 inception glc19 lter predictions LIRMM 25 26968 0.0205 run 14 sergiu atodiresei 26 26964 0.0191 run 10 sergiu atodiresei 27 26961 0.0190 run 7 sergiu atodiresei 28 26971 0.0184 run 17 sergiu atodiresei 29 26967 0.0180 run 13 sergiu atodiresei 30 26960 0.0168 run 6 sergiu atodiresei 31 27062 0.0159 run 20 sergiu atodiresei 32 26958 0.0146 run 3 sergiu atodiresei 33 26970 0.0102 run 16 sergiu atodiresei 34 26969 0.0099 run 15 sergiu atodiresei 35 26972 0.0089 run 18 sergiu atodiresei 36 26963 0.0079 run 9 sergiu atodiresei 37 26965 0.0068 run 11 sergiu atodiresei 38 26926 0.0067 run 4 sergiu atodiresei 39 26973 0.0064 run 19 sergiu atodiresei 40 26959 0.0063 run 5 sergiu atodiresei 41 26962 0.0062 run 8 sergiu atodiresei 42 26957 0.0061 run 2 sergiu atodiresei 43 26966 0.0058 run 12 sergiu atodiresei 44 26956 0.0058 run 1 sergiu atodiresei
Table 1. Table of Result of GeoLifeCLEF 2019 challenge second metric they use the Mean Reciprocal Rank (MRR) to compare with the GeoLifeCLEF 2018 challenge.
For this challenge we submitted 4 di erent runs. We can see the global result in Figure 2 and Table 1.
{ The rst one: full noplant predictions is a CNN trained with every data set (plantnet, no plant, glc 18). We used pl@ntnet database without any lters (2,367,145 plant species occurrences), train and test occurrences from last year's challenge ( le GLC 2018.csv) and occurrences from other kingdoms (such as mammals, birds, amphibians, insects, fungus etc.) from the GBIF database ( le noPlant.csv). There are 34,375 di erent classes, so this CNN has a larger last layer output, but for the test we let it choose only the plant classes which are provided by the challenge. This method showed the best result with a Top 30 of 0.1769 and a MRR of 0.031. { The second submission, full predictions is a CNN trained with plantnet data set and the data set of last year challenge. This means that we only have plants to train our model, we have 3,859 classes. It is the 5 th submission of the challenge, after SaraSi models with a top 30 of 0.1364. { The two other submissions were not as good (plcomplete predictions and inception glc19 lter predictions), we sampled our train set as explained in the challenge protocol for the test set. Therefore, we have reduced our data, our model didn't capture as much information as the other models.
In this case, it seems that the more data available to the CNN, the better the results. It seems that our model is learning from the species from another kingdoms that live in the same area. Indeed, our model train with more than only plant occurrences showed a better result for predicting plants. Here, the CNN has more classes but can classify plants even better. We can deduct from this that our CNN network not only captures environmental information but also, the interaction between di erent species, we can imagine that it can also learn from the information of the species that live around the plants and perhaps from the ecological niche of an occurrence. 5
This paper describes our participation in GeoLifeCLEF challenge to evaluate location-based species prediction models. We compared Convolutional Neural Network trained with di erent data sets. The main conclusion of our study is that the convolutional neural network model is the most e cient model and it can learn information not only from environmental rasters but also from interaction between species of other kingdoms. Indeed, it achieved the best performance of the whole GeoLifeCLEF challenge when we trained our model with other kingdom observations.
In future work, we will attempt to better understand what information the CNN does capture from di erent data sets and how it could be improved.