Automatic image annotation : the case of deforestation (2018)

Abstract: Automatic image annotation : the case of deforestation. This paper aims to present the state of the art of the methods that are used for automatic annotation of earth observation image for deforestation detection. We are interested in the various challenges that the field covers and we present the state of the art methods and the future research that we are considering.

## introduction according to the national geographic, forest covers about 30% of the planet. forest ecosystems play an essential role in supporting life on the earth such as supplying wood, water regulation, preventing storms and soil erosion and forests store rare genetic resources for our planet. deforestation affects the environment in a multitude of ways. the most obvious effects are global warming and loss of biodiversity. from the photosynthetic function of trees, forests release oxygen and absorb carbon dioxide. the fewer forests, the more carbon dioxide entering the atmosphere, increasing the speed of global warming. in addition, earth's forests are home to over 80% of plants and animals but deforestation destroys these habitats, diminishing biodiversity and causing the extinction of four to six thousand rainforest species every year @xcite . direct causes of deforestation are agricultural expansion, logging and wood extraction, bio fuels from palm oil, infrastructural expansion such as road and urbanization, and mining @xcite (see also http://www . ## annotation of land cover image annotation is a general problem which is not specific to forest detection but rather that covers a large variety of domains such as information extraction from medical images for disease detection @xcite , @xcite , and image retrieval @xcite . in the case of deforestation detection, what we need is land cover information. the automatic annotation of earth observation images to associate land cover types with areas is typically done with classification methods. our intention is not to provide a state of the art of image classification methods but rather to detail how these methods have been used in the case of land cover annotation with an emphasis on deforestation detection. as in many fields, deep learning has revolutionised the domain of image annotation for deforestation detection in remote sensing. without aiming to be exhaustive, in this section, we first present some of the approaches that have been developed for land cover annotation of satellite imagery before the deep neural networks arose, then we present how deep learning is used in this domain. ## change detection in remote sensing images in remote sensing, the use of satellite images to assess and then map deforestation is one of the applications of change detection. two methods are applied for change detection with optical images : image to image change detection and time series analysis. image-to-image change detection requires a minimum of two images captured at different times while time series analysis requires a series 6. http://blog.kaggle.com/2017/10/17/planet-understanding-the-amazon-from-space-1st7. https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/ discussion/36732 c atala 2018 of images captured over a period of time. image-to-image change detection is more commonly used. performing change detection over long time periods may require the use of images captured by different remote sensing devices with different characteristics which then requires data fusion techniques to combine these data together. ## research questions from our readings and from related work, we elaborate what we think are the main research questions at this stage in the domain of automatic detection of deforestation using earth observation imagery. ## conclusion this paper presented the state of the art related to automatic detection of deforestation using earth observation images from remote sensors. we present the methods that have been developed in the two main steps of automatic detection of deforestation : image annotation which includes image segmentation and classification, and change detection. for both steps, we chose to present first the methods that were developed before deep neural networks then the methods which use deep learning. as in many other fields, neural networks are now very commonly used in remote sensing. finally, we mentioned the research questions we would like to tackle in our future work and for two of them, namely data fusion and transfer learning, we detailed related work.