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Diego Marcos

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4 papers

1 author row

AAAI Conference 2025 Conference Paper

Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy

Ron van Bree
Diego Marcos
Ioannis N. Athanasiadis

Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural disparities. We evaluate our hybrid model in an extensive case study predicting cherry tree phenology in Japan, South Korea and Switzerland. Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years. Additionally, the neural network's adaptability facilitates parameter learning for specific tree varieties, enabling robust generalization to new sites without site-specific recalibration. This hybrid model leverages both biophysical constraints and data-driven flexibility, offering a promising avenue for accurate and interpretable phenology modeling.

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NeurIPS Conference 2024 Conference Paper

Coarse-to-Fine Concept Bottleneck Models

Konstantinos P. Panousis
Dino Ienco
Diego Marcos

Deep learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinders their confident deployment in real-world safety-critical tasks. This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly interpretable decision making process with respect to human understandable concepts, on two levels of granularity. To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models, and (ii) an innovative formulation for coarse-to-fine concept selection via data-driven and sparsity inducing Bayesian arguments. Within this framework, concept information does not solely rely on the similarity between the whole image and general unstructured concepts; instead, we introduce the notion of concept hierarchy to uncover and exploit more granular concept information residing in patch-specific regions of the image scene. As we experimentally show, the proposed construction not only outperforms recent CBM approaches, but also yields a principled framework towards interpetability.

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NeurIPS Conference 2024 Conference Paper

GeoPlant: Spatial Plant Species Prediction Dataset

Lukas Picek
Christophe Botella
Maximilien Servajean
César Leblanc
Rémi Palard
Théo Larcher
Benjamin Deneu
Diego Marcos

The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological knowledge and conservation efforts. To fill this gap, Species Distribution Models (SDMs) predict species across space from spatially explicit features. Yet, they face the challenge of integrating the rich but heterogeneous data made available over the past decade, notably millions of opportunistic species observations and standardized surveys, as well as multi-modal remote sensing data. In light of that, we have designed and developed a new European-scale dataset for SDMs at high spatial resolution (10--50m), including more than 10k species (i. e. , most of the European flora). The dataset comprises 5M heterogeneous Presence-Only records and 90k exhaustive Presence-Absence survey records, all accompanied by diverse environmental rasters (e. g. , elevation, human footprint, and soil) traditionally used in SDMs. In addition, it provides Sentinel-2 RGB and NIR satellite images with 10 m resolution, a 20-year time series of climatic variables, and satellite time series from the Landsat program. In addition to the data, we provide an openly accessible SDM benchmark (hosted on Kaggle), which has already attracted an active community and a set of strong baselines for single predictor/modality and multimodal approaches. All resources, e. g. , the dataset, pre-trained models, and baseline methods (in the form of notebooks), are available on Kaggle, allowing one to start with our dataset literally with two mouse clicks.

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TMLR Journal 2022 Journal Article

Attribute Prediction as Multiple Instance Learning

Diego Marcos
Aike Potze
Wenjia Xu
Devis Tuia
Zeynep Akata

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always straightforward, as per-image ground truth attributes are often not available. We propose applying the Multiple Instance Learning (MIL) paradigm to attribute learning (AMIL) while only using class-level labels. We allow the model to under-predict the positive attributes, which may be missing in a particular image due to occlusions or unfavorable pose, but not to over-predict the negative ones, which are almost certainly not present. We evaluate it in the zero-shot learning (ZSL) setting, where training and test classes are disjoint, and show that this also allows to profit from knowledge about the semantic relatedness of attributes. In addition, we apply the MIL assumption to ZSL classification and propose MIL-DAP, an attribute-based zero-shot classification method, based on Direct Attribute Prediction (DAP), to evaluate attribute prediction methods when no image-level data is available for evaluation. Experiments on CUB-200-2011, SUN Attributes and AwA2 show improvements on attribute detection, attribute-based zero-shot classification and weakly supervised part localization.

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