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Biology subjects

Tabik, S.

Publications and source records attributed to Tabik, S..

4 recordsLinked to original sources

What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media

High mountain protected areas (PAs) are increasingly recognized not only for their role in conserving biodiversity but also for their contribution to the provision of cultural ecosystem services (CES). Despite their relevance, CES remain underrepresented in conservation planning, particularly due to challenges in quantifying their spatial distribution. This study combines geolocated social media data and ecological niche models (ENMs) to assess the spatial patterns and key drivers of CES supply across eight mountain PAs spanning distinct biogeographical regions in Spain and Portugal. Using deep learning techniques to classify more than 200,000 photographs into ten CES categories, we evaluated model performance under two modeling approaches and identified the most influential environmental and social predictors. Most CES categories exhibited good model performance (Boyce index > 0.5), though variation existed across services and regions. Nature & Landscape and Gastronomy CES showed strong associations with park boundaries and human settlements, respectively, while Religious and Cultural CES were spatially linked to culturally significant landmarks.. Our findings demonstrate the potential of combining social media data with ENMs to map CES distributions and reveal both universal and context-dependent drivers. This approach offers valuable insights for integrating CES into PA management and spatial planning, supporting more holistic and culturally inclusive conservation strategies.

ecology↗

DiatomNet: An automatic Diatom genus identification system through microscopic images and Deep Learning

Diatoms are microscopic organisms belonging to the algae kingdom. They adapt to the ecosystem and modify their shape and texture depending on hundreds of ecosystem variables. Hence, these micro-organism are considered as the most accurate indicator to measure water quality. Commonly, the recognition of the class of diatoms in a microscopic image has always been done by expert biologists knowledgeable about the morphometric characteristics of these organisms. This work proposes a new automatic diatom genus recognition system from microscopic images using state-of-the-art deep CNNs. In particular, 1) we developed a public high quality database organized into 44 genus-level diatom classes, 2) designed a robust diatom classification model, and 3) provided a user-friendly interface to utilize our automatic diatom recognition tool.

bioinformatics↗

Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there is still low consistency among LULC products due to low accuracy for some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the consensus of 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v1.1 contains 195572 RGB images organized into 29 global LULC mapping classes. Each image is a tile that has 224 x 224 pixels at 10 x 10 m spatial resolution and was built as a cloud-free composite from all Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC type annotation per image, together with level of consensus, reverse geo-referencing, and global human modification index. Sentinel2GlobalLULC is optimized for the state-of-the-art Deep Learning models to provide a new gate towards building precise and robust global or regional LULC maps.

ecology↗

Automatic whale counting in satellite images with deep learning

Despite their interest and threat status, the number of whales in worlds oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution orthoimages. Since deep convolutional neural networks (CNNs) achieve great performance in object-recognition in images, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales from space based on open data and tools. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 84% in detecting and 97% in counting 80 whales. Applying this cost-effective method worldwide could facilitate the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process.

ecology↗