Ana Paula Marques Ramos
@fct.unesp.br
Ph.D. Assistant Professor in the Department of Cartography
São Paulo State University (Unesp)
Ph.D. in Cartographic Sciences from the São Paulo State University (Unesp). Currently, she is an Assistant Professor at Unesp in the Department of Cartography. Her experience is regarding Geosciences, with an emphasis on Remote Sensing of Vegetation, and spatial analysis. Recently she started to develop applied research by integrating Geomatics (mainly Remote Sensing of Vegetation, and spatial analysis) and Machine Learning (shallow and deep learning) areas into environmental and precision agriculture issues studies. She is a CNPq Research Productivity Scholarship (2021-2024) in the area of Geosciences (PQ level -two).
EDUCATION
2011-04-01 to 2015-04-30 | Ph.D. (Cartographic Sciences Post-Graduation Program);
2009-03-01 to 2011-03-31 | Master (Cartographic Sciences Post-Graduation Program);
2004-03-01 to 2008-12-31 | Graduated Cartographic Engineer (Cartographic Engineering)
RESEARCH INTERESTS
Develops research in the Geomatics area, focusing on Remote Sensing of Vegetation and Cartography. Has been involved in research focused on the application of Machine Learning (shallow and deep algorithms) in Remote Sensing data.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, Jonathan Li, and José Marcato
Elsevier BV
Lucas Prado Osco, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, and José Marcato Junior
MDPI AG
Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. These are known as Visual LLMs and one notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. These models’ abilities to process images based on textual inputs can revolutionize diverse fields, and while their application in the remote sensing domain remains unexplored, it is important to acknowledge that novel implementations are to be expected. Thus, this is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.
Michelle Taís Garcia Furuya, Danielle Elis Garcia Furuya, Lucas Yuri Dutra de Oliveira, Paulo Antonio da Silva, Rejane Ennes Cicerelli, Wesley Nunes Gonçalves, José Marcato Junior, Lucas Prado Osco, and Ana Paula Marques Ramos
Springer Science and Business Media LLC
Samuel Fernando Schwaida, Rejane Ennes Cicerelli, Tati de Almeida, Edson Eyji Sano, Carlos Henrique Pires, and Ana Paula Marques Ramos
Springer Science and Business Media LLC
Diogo Nunes Gonçalves, José Marcato, André Caceres Carrilho, Plabiany Rodrigo Acosta, Ana Paula Marques Ramos, Felipe David Georges Gomes, Lucas Prado Osco, Maxwell da Rosa Oliveira, José Augusto Correa Martins, Geraldo Alves Damasceno,et al.
Elsevier BV
Bruna Maria Casachi Bernardes de Melo Carapeba, Sérgio Marques Costa, Rogério Giuffrida, Ana Paula Alves Favareto, Ana Paula Marques Ramos, Fabíola de Azevedo Mello, Marcus Vinicius Pimenta Rodrigues, and Renata Calciolari Rossi
Universidade de São Paulo. Agência de Bibliotecas e Coleções Digitais
O objetivo deste trabalho foi analisar a distribuição espaço-temporal dos pacientes com cardiopatias congênitas atendidos no Ambulatório de Cardiologia Pediátrica do Hospital de referência do Oeste Paulista. Realizamos um estudo retrospectivo com análise de dados de base eletrônica e prontuários dos pacientes diagnosticados com cardiopatiacongênita entre os períodos de julho de 2013 a julho de 2018. Foram selecionados 298 prontuários para análise das variáveis de CID-10, gênero, distribuição espacial e série temporal. Foi possível observar que os defeitos septais foram as cardiopatias mais prevalentes, não houve diferença entre os gêneros. Notou-se aumento do diagnóstico a partir de 2014, com implementação do teste do coraçãozinho e 51% dos casos eram da cidade de Presidente Prudente,com maior concentração de casos na região do parque industrial. Há uma relação na incidência das malformações cardíacas com o meio ambiente desfavorável. Os resultados encontrados podem guiar políticas de saúde pública, visando reduzir a exposição da população mais vulnerável, na busca da melhora nos índices de saúde.
Mário de Araújo Carvalho, José Marcato, José Augusto Correa Martins, Pedro Zamboni, Celso Soares Costa, Henrique Lopes Siqueira, Márcio Santos Araújo, Diogo Nunes Gonçalves, Danielle Elis Garcia Furuya, Lucas Prado Osco,et al.
Elsevier BV
Diogo Nunes Gonçalves, Plabiany Rodrigo Acosta, Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, Michelle Taís Garcia Furuya, Jonathan Li, José Marcato Junior, Hemerson Pistori, and Wesley Nunes Gonçalves
Elsevier BV
Lucas Prado Osco, Danielle Elis Garcia Furuya, Michelle Taís Garcia Furuya, Daniel Veras Corrêa, Wesley Nunes Gonçalvez, José Marcato Junior, Miguel Borges, Maria Carolina Blassioli-Moraes, Mirian Fernandes Furtado Michereff, Michely Ferreira Santos Aquino,et al.
Elsevier BV
Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta, Diogo Nunes Gonçalves, José Marcato Junior, Ana Paula Marques Ramos, Edson Takashi Matsubara, Zhipeng Luo, Jonathan Li, Jonathan de Andrade Silva,et al.
Elsevier BV
Caroline Souza Senkiio, Ana Paula Marques Ramos, Silvio Jorge Coelho Simões, and Tatiana Sussel Gonçalves Mendes
Springer Science and Business Media LLC
Patrik Olã Bressan, José Marcato Junior, José Augusto Correa Martins, Maximilian Jaderson de Melo, Diogo Nunes Gonçalves, Daniel Matte Freitas, Ana Paula Marques Ramos, Michelle Taís Garcia Furuya, Lucas Prado Osco, Jonathan de Andrade Silva,et al.
Elsevier BV
Maximilian Jaderson de Melo, Diogo Nunes Gonçalves, Marina de Nadai Bonin Gomes, Gedson Faria, Jonathan de Andrade Silva, Ana Paula Marques Ramos, Lucas Prado Osco, Michelle Taís Garcia Furuya, José Marcato Junior, and Wesley Nunes Gonçalves
Elsevier BV
Ana Paula Marques Ramos, Felipe David Georges Gomes, Mayara Maezano Faita Pinheiro, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalvez, José Marcato Junior, Mirian Fernandes Furtado Michereff, Maria Carolina Blassioli-Moraes, Miguel Borges, Raúl Alberto Alaumann,et al.
Springer Science and Business Media LLC
Samara Peruzzo Gusman, Ana Paula Marques Ramos, and Alba Regina Azevedo Arana
Universidade Federal de Goias
A produção desordenada do espaço urbano gera consequências negativas no bem-estar e na qualidade de vida dos habitantes, promovendo, danos ambientais que podem der irreversíveis. O artigo objetiva analisar a percepção ambiental no ambiente universitário e entender como a legibilidade do espaço ajuda na percepção ambiental. Trata-se de uma pesquisa aplicada e exploratória, utilizando trabalho de campo de abordagem qualitativa, os dados foram obtidos através das entrevistas e observação participante. Os entrevistados foram divididos em três classes: aluno, professor e colaborador. Eles foram questionados sobre o significado do Campus, elementos distintivos do local, indicações do percurso mais comum feito pelos entrevistados e justificativas das indicações dos elementos distintivos. Predominaram definições positivas nas classificações sobre o significado do Campus, sendo: amplo e arborização/árvores, citadas por 25% dos entrevistados. Os resultados obtidos indicam que os mapas mentais foram instrumentos importantes e eficazes, para identificar a construção do conhecimento espacial e legibilidade por parte dos estudantes, professores e servidores do campus. Palavras-chave: Paisagem. Espaço urbano. Universidade. Areas verdes.
Danielle Elis Garcia Furuya, Lingfei Ma, Mayara Maezano Faita Pinheiro, Felipe David Georges Gomes, Wesley Nunes Gonçalvez, José Marcato Junior, Diego de Castro Rodrigues, Maria Carolina Blassioli-Moraes, Mirian Fernandes Furtado Michereff, Miguel Borges,et al.
Elsevier BV
Luciene Sales Dagher Arce, Lucas Prado Osco, Mauro dos Santos de Arruda, Danielle Elis Garcia Furuya, Ana Paula Marques Ramos, Camila Aoki, Arnildo Pott, Sarah Fatholahi, Jonathan Li, Fábio Fernando de Araújo,et al.
Springer Science and Business Media LLC
AbstractAccurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.
Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Regimar Garcia dos Santos, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Lucas Prado Osco, Wesley Nunes Gonçalves, Alexsandro Monteiro Carneiro,et al.
MDPI AG
In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.
Diego Bedin Marin, Gabriel Araújo e Silva Ferraz, Lucas Santos Santana, Brenon Diennevan Souza Barbosa, Rafael Alexandre Pena Barata, Lucas Prado Osco, Ana Paula Marques Ramos, and Paulo Henrique Sales Guimarães
Elsevier BV
Abstract Coffee leaf rust (CLR) is one of the most devastating leaf diseases in coffee plantations. By knowing the symptoms, severity, and spatial distribution of CLR, farmers can improve disease management procedures and reduce losses associated with it. Recently, Unmanned Aerial Vehicles (UAVs)-based images, in conjunction with machine learning (ML) techniques, helped solve multiple agriculture-related problems. In this sense, vegetation indices processed with ML algorithms are a promising strategy. It is still a challenge to map severity levels of CLR using remote sensing data and an ML approach. Here we propose a framework to detect CLR severity with only vegetation indices extracted from UAV imagery. For that, we based our approach on decision tree models, as they demonstrated important results in related works. We evaluated a coffee field with different infestation classes of CLR: class 1 (from 2% to 5% rust); class 2 (from 5% to 10% rust); class 3 (from 10% to 20% rust), and; class 4 (from 20% to 40% rust). We acquired data with a Sequoia camera, producing images with a spatial resolution of 10.6 cm, in four spectral bands: green (530–570 nm), red (640–680 nm), red-edge (730–740 nm), and near-infrared (770–810 nm). A total of 63 vegetation indices was extracted from the images, and the following learners were evaluated in a cross-validation method with 10 folders: Logistic Model Tree (LMT); J48; ExtraTree; REPTree; Functional Trees (FT); Random Tree (RT), and; Random Forest (RF). The results indicated that the LMT method contributed the most to the accurate prediction of early and several infestation classes. For these classes, LMT returned F-measure values of 0.915 and 0.875, thus being a good indicator of early CLR (2 to 5% of rust) and later stages of CLR (20 to 40% of rust). We demonstrated a valid approach to model rust in coffee plants using only vegetation indices and ML algorithms, specifically for the disease's early and later stages. We concluded that the proposed framework allows inferring the predicted classes in remaining plants within the sampled area, thus helping the identification of potential CLR in non-sampled plants. We corroborate that the decision tree-based model may assist in precision agriculture practices, including mapping rust in coffee plantations, providing both an efficient non-invasive and spatially continuous monitoring of the disease.
Lucas Prado Osco, José Marcato Junior, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gonçalves, and Jonathan Li
Elsevier BV
Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms’ applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both “deep learning” and “UAV remote sensing” thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.
José Augusto Correa Martins, Keiller Nogueira, Lucas Prado Osco, Felipe David Georges Gomes, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Diego André Sant’Ana, Ana Paula Marques Ramos, Veraldo Liesenberg, Jefersson Alex dos Santos,et al.
MDPI AG
Urban forests are an important part of any city, given that they provide several environmental benefits, such as improving urban drainage, climate regulation, public health, biodiversity, and others. However, tree detection in cities is challenging, given the irregular shape, size, occlusion, and complexity of urban areas. With the advance of environmental technologies, deep learning segmentation mapping methods can map urban forests accurately. We applied a region-based CNN object instance segmentation algorithm for the semantic segmentation of tree canopies in urban environments based on aerial RGB imagery. To the best of our knowledge, no study investigated the performance of deep learning-based methods for segmentation tasks inside the Cerrado biome, specifically for urban tree segmentation. Five state-of-the-art architectures were evaluated, namely: Fully Convolutional Network; U-Net; SegNet; Dynamic Dilated Convolution Network and DeepLabV3+. The experimental analysis showed the effectiveness of these methods reporting results such as pixel accuracy of 96,35%, an average accuracy of 91.25%, F1-score of 91.40%, Kappa of 82.80% and IoU of 73.89%. We also determined the inference time needed per area, and the deep learning methods investigated after the training proved to be suitable to solve this task, providing fast and effective solutions with inference time varying from 0.042 to 0.153 minutes per hectare. We conclude that the semantic segmentation of trees inside urban environments is highly achievable with deep neural networks. This information could be of high importance to decision-making and may contribute to the management of urban systems. It should be also important to mention that the dataset used in this work is available on our website.
Lucas Prado Osco, Keiller Nogueira, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Lucio André de Castro Jorge, José Marcato Junior, and Jefersson Alex dos Santos
Springer Science and Business Media LLC
Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation fields from the remaining objects in a multispectral scene is a difficult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following five state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3 + . The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-affected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation fields.
Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, Alexandre Dias, Juliana Batistoti, Mauricio de Souza, Felipe David Georges Gomes, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Veraldo Liesenberg,et al.
Elsevier BV
Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems.
Antonio C. Titosse, Marcus Vinícius Pimenta Rodrigues, Ana Paula Marques Ramos, Lucas Prado Osco, Rogério Giuffrida, Elivelton Da Silva Fonseca, and Alba Regina Azevedo Arana
Universidad de Costa Rica
Objective: Carry out a spatial-temporal characterization of the incidence of tuberculosis (TB) in Maputo, Mozambique. Method: a descriptive ecological study of tuberculosis cases reported in an information system. The annual mean incidence rate and the number of TB notification cases in the municipality of Maputo from 2011 to 2016 were analyzed. Descriptive statistics were used with calculations of measures of central tendency (mean) and an application of the Poisson linear regression model. Trimester notifications were stratified by district, clinical form, and age group. The quarterly average temperature of the evaluated area was added as a covariate in the model seasonal. Results: 34,623 TB cases were notified from 2011 to 2016, with a trimester average of 1,443 cases. The average annual incidence was higher in the Kampfumo district, with 909.8 per 100 thousand inhabitants (95% CI 854.1 - 968.2); almost twice as much as the incidence of the municipality of Maputo, 527.8 (95% CI 514, 3-541.6), and the country of Mozambique, 551 (95% CI 356 - 787). The clinical diagnosis of the tested cases was higher concerning the bacteriological diagnosis; 44%, and 35%, respectively. Conclusion: Maputo had similar incidence rates to the country of Mozambique, however, there was a heterogeneity rate by district and a reduction in the number of TB cases in both the general population (not co-infected with HIV) and those over 15 years old, being higher in the first trimester.
Jose Marcato Junior, Pedro Zamboni, Mariana Campos, Ana Ramos, Lucas Osco, Jonathan Silva, Wesley Goncalves, and Jonathan Li
IEEE
The integration of photogrammetry and deep learning methods can be powerful for Earth observation applications. Photogrammetry techniques allow the achievement of detailed geospatial products with em-level positional accuracy. Deep learning enables automatic image classification, segmentation, and object detection. For instance, when dealing with a large data set, photogrammetric processing steps, such as image orientation and dense point cloud generation, results in high computational costs. In contrast, deep learning methods are fast in the inference step. Here, we explore the complementarity of deep learning and photogrammetry, aiming to generate accurate and fast geospatial information. The main aim is to discuss the possibilities of using deep learning in the photogrammetric process. We conduct experiments to present the potential of the Mask R-CNN method trained on the COCO dataset to generate masks, essential to remove image observations from moving objects during the orientation (alignment) step.