Aerial Photographs Research Articles

Detecting GNSS spoofing and Re-localization on UAV based on imagery matching

Abstract Due to rapid advancements in global navigation satellite system (GNSS), computer, and microelectronics technologies, there is a growing popularity and widespread promotion of high-performance, cost-effective intelligent UAVs across various applications. Recently, GNSS spoofing attacks have emerged as a significant obstacle hindering the long-term development of UAVs. UAVs rely heavily on unprotected GNSS signals for navigation, making them highly vulnerable to spoofing. This paper presents an algorithm that employs image matching for detecting GNSS spoofing and re-localization on UAVs using a deep learning methodology. This method functions autonomously solely reliance on camera and does not require alterations to the antenna configuration. Utilizing a camera-equipped UAV, we evaluate the likeness between real-time aerial photographs and satellite imagery by leveraging the position information provided by UAV. By identifying disparities between images taken by a spoofing affected drone and authentic ones, the spoofing can be identified using deep neural network models. Upon detecting spoofing, this paper presents a vision-based re-localization method for UAVs. Experimental results demonstrate an approximately 88.4% accuracy of our model in detecting GNSS spoofing attacks within 100 ms and 88% success rate of re-localization with the accuracy of less than 15 m. Our algorithm exclusively depends on publicly accessible satellite imagery, offering an intelligent and efficient approach for detecting UAV GNSS spoofing and re-localization in GNSS denied environment.

Investigation on Miage/Brenva Glaciers in The Alps From 50s to-date Based on Remote-Sensing Data

Abstract. The Miage and Brenva Glaciers in Mount Blanc Massif (Italy) are two of the major glaciers in the Alps. To assess the evolution of these glaciers, we use multiple data sources from remote sensing, and we adopt different techniques to extract important information on their conditions during a long period (i.e., ~60 years). The first approach, which is based on the use of aerial photos and satellite imagery (SPOT 6), allows assessing the volume change of Miage Glacier over the period 1952–2014. The second approach, which is based on the use of optical satellite observations such as Landsat 5/8/9 and Sentinel 2, aims to evaluate the spatial variability and the temporal trends of the snow cover of both glaciers from 1984 up to now. The achieved results, that are coherent with the ones reported by the scientific literature, show that volumetric changes of Miage Glacier underwent a period of gain between 1975–1991, followed by an impressive decay phase. Concerning the snow cover analysis, our findings highlight that for both glaciers the area covered by snow reduces persistently year-by-year, especially in the case of the Miage Glacier.

Development of methodology for calculating flooded area and flood volume in small urban areas based on unmanned aerial vehicle images

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers.

The emerging middle ground: A case study of vernacular Hakka settlements in the peri-urban areas of southern China through an architectural heritage analysis

Rapid urban expansion is transforming rural areas globally, prompting scholars to challenge the traditional urban-rural dichotomy and focus on transitional zones such as the rural-urban fringe and peri-urban areas. This article documents the evolution of architectural morphology in traditional Hakka settlements in peri-urban areas of southern China and coins the hybrid architectural landscape combined with urban and rural characteristics in these ambiguous territories as the “Middle Ground.” Fieldwork was conducted in four Hakka villages in Heyuan, northeast Guangdong, China, between 2021 and 2023. An explorative case study was employed to analyze the architectural morphology of the Middle Ground by mapping village fabric, dwellings, and street markets through aerial photography. A participatory perspective was also incorporated, involving observation, interviews, and documentation of the daily life of local villagers. This research examines how authority and mobility have shaped the Middle Ground, potentially leading to a state of “placelessness,” and explores the role of rural heritage in this context. This research contributes to the broader discourse on peri-urban areas by offering an architectural heritage atlas as an addition to existing research.

Coherence and Intensity Statistical Mapping of Earthquake Affected Areas using Sentinel-1 Images

Abstract. The January 1, 2024, Noto Peninsula earthquake left many devastated areas. Right after the event, aerial photography provided the initial assessment but covered only specific areas. Satellite images with optical sensors that are freely available are limited by spatial resolution and oftentimes covered by cloud. The Sentinel-1 Synthetic Aperture Radar (SAR) due to its all-weather sensing capability proved to be useful in mapping the extent of the affected areas. This study mapped Wajima City where earthquake aftereffects were severe as reported using coherence and intensity mapping. Pre- and post-event images were used to rapidly identify affected areas. Based on the result, taking the coherence of one pre- and one post-event images can give an impression of wide earthquake damage. The coherence of two post-event images compared with two pre-event images provides a better estimate of affected areas. Taking multiple pre- and post- event images improved the map because the large variation of the intensity is minimized through time-series averaging. Using a simple RGB composite, the affected areas were mapped. Threshold mapping was also used to extract collapsed buildings/houses using a training dataset. The mean coherence difference and mean intensity difference between pre- and post-event images were the two variables used. The computed minimum threshold for the mean coherence difference was 0.35. However, the average not the minimum of the mean intensity difference of 3.0 was used as initial value. Using mean coherence difference>=0.35 gave a high accurate prediction of collapsed buildings at 73.47% but also has false identification of 30.95%. To negate the overprediction, the mean intensity difference<=4.0 was integrated. The result lowered the overprediction to 20.75% but also lowered the accuracy to 64.97%. This assessment was only conducted in the fire-razed morning market in Wajima City.

A comprehensive epidemiological approach documenting an outbreak of H5N1 highly pathogenic avian influenza virus clade 2.3.4.4b among gulls, terns, and harbor seals in the Northeastern Pacific

Highly pathogenic avian influenza viruses (HPAIV) H5N1 clade 2.3.4.4b continue to have unprecedented global impacts on wild birds and mammals, with especially significant mortality observed in colonial surface-nesting seabirds and in some marine mammal species. In July of 2023 H5N1 HPAIV 2.3.4.4b was detected in Caspian terns nesting on Rat Island, Washington USA. An estimated 1,800–1,900 adult terns populated the breeding colony, based on aerial photographs taken at the start of the outbreak. On a near-weekly basis throughout July and August, we counted and removed carcasses, euthanized moribund birds, and collected swab and tissue samples for diagnostic testing and next-generation sequencing. We directly counted 1,101 dead Caspian tern adults and 520 dead chicks, indicating a minimum 56% loss of the adult colony population and potential impacts to reproductive success. Combining the observed mortality on Rat Island with HPAI-related Caspian tern deaths recorded elsewhere in Washington and Oregon, we estimate that 10–14% of the Pacific Flyway population was lost in the summer of 2023. Comparatively few adult Glaucous-winged gulls (hybrids) nesting on Rat Island died (~3% of the local population), although gull chick mortality was high. Sixteen harbor seals in the immediate or nearby area stranded during the outbreak, and H5N1 HPAIV was detected in brain and/or lung tissue of five seals. These cases are the first known detections of HPAIV in a marine mammal on the Pacific coast of North America. Phylogenetic analyses support the occurrence of at least three independent avian-mammalian virus spillover events (tern or gull to harbor seal). Whole genome sequencing indicated that H5N1 HPAIV may have been introduced to Washington from Caspian terns in Oregon. Ongoing monitoring and surveillance for H5N1 HPAIV in the marine environment is necessary to understand the epidemiology of this virus, assess conservation impacts to susceptible species, and provide support for data-driven management and response actions.

Geological and Geomorphological Characterization of the Anthropogenic Landslide of Pie de la Cuesta in the Vitor Valley, Arequipa, Peru

This study presents the geological and geomorphological characterization of the Pie de la Cuesta landslide, a large (>60 ha) slow-moving (up 4.5 m/month) landslide in Southern Peru. The landslide has been active since 1975 and underwent a significant re-activation in 2016; the mass movement has caused the loss of property and agricultural land and it is currently moving, causing further damage to property and land. We use a combination of historical aerial photographs, satellite images and field work to characterize the landslide’s geology and geomorphology. The landslide is affecting the slope of the Vitor Valley, constituted by a coarsening upward sedimentary sequence transitioning from layers of mudstone and gypsum at the base, to sandstone and conglomerate at the top with a significant ignimbrite layer interbedded within conglomerates near the top of the sequence. The landslide is triggered by an irrigation system that provides up to 10 L/s of water infiltrating the landslide mass. This water forms two groundwater levels at lithological transitions between conglomerates and mudstones, defining the main failure planes. The landslide is characterized by three main structural domains defined by extension, translation and compression deformation regimes. The extensional zone, near the top of the slope, is defined by a main horst–graben structure that transitions into the translation zone defined by toppling and disaggregating blocks that eventually become earth flows that characterize the compressional zone at the front of the landslides, defined by thrusting structures covering the agricultural land at the valley floor. The deformation rates range from 8 cm/month at the top of the slope to 4.5 m/month within the earth flows. As of May 2023, 22.7 ha of potential agricultural land has been buried.

AN ASSESSMENT OF LAND USE AND LAND COVER CHANGE IN MANGA GRASSLAND OF YOBE STATE, NIGERIA

Human activities and natural events continually reshape land use patterns, underscoring the critical need for accurate land use information in various applications such as natural resource management and environmental monitoring. Land use change has become pivotal in current strategies for these purposes globally. The rapid advancements in land use mapping have spurred increased studies worldwide, aiming to assess and monitor the extent and health of forests, grasslands, and agricultural lands. Remotely sensed data, including aerial photographs and satellite imagery, are indispensable for extracting precise land use change information. This study focusing on Manga grassland (Machina, Yusufari, and Yunusari LGAs), satellite images from Landsat TM (1988), Landsat ETM (1998), Landsat ETM+ (2008), and Landsat 8 (2018) for image classification. The study employed supervised classification methods to analyse land use changes, revealing significant alterations in vegetation cover over the study period. In conclusion, it was observed that observed that vegetation cover increases from north toward the south follow the trend of rain fall, over-cutting, over grazing, and other climatic factors, which determines species distribution. Vegetation degradation is detected more in north boundary Niger republic. This may be due to southern flow of the Sahara Desert into the northern part of Nigeria around the study area. Data analysis shows that land use land cover had been changing through the study zones and this change had negative impact on natural resource development. Finally, it was recommended that the development of suitable and adequate mean of awareness for local community on the importance of species...

Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.

National-Scale Detection of New Forest Roads in Sentinel-2 Time Series

The Norwegian Environment Agency is responsible for updating a map of undisturbed nature, which is performed every five years based on aerial photos. Some of the aerial photos are already up to five years old when a new version of the map of undisturbed nature is published. Thus, several new nature interventions may have been missed. To address this issue, the timeliness and mapping accuracy were improved by integrating Sentinel-2 satellite imagery for the detection of new roads across Norway. The focus on new roads was due to the fact that most new nature interventions include the construction of new roads. The proposed methodology is based on applying U-Net on all the available summer images with less than 10% cloud cover over a five-year period, with an aggregation step to summarize the predictions. The observed detection rate was 98%. Post-processing steps reduced the false positive rate to 46%. However, as the false positive rate was still substantial, the manual verification of the predicted new roads was needed. The false negative rate was low, except in areas without vegetation.

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data

High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area.

Invisible favelas

This article presents the first part of the results of a study on the presence and/ or absence of Rio de Janeiro’s favelas in the city’s cartography. Different data sources were used: research in the municipal archives—including the Rio’s first aerial photographs ever made, cartographic records and infrastructure maps from the 1920s and 1930s; research in building codes; and research on digital cartographic platforms. The aim of the article is to investigate when and how the urban phenomenon of favelas officially appears in the city’s records and to reflect on how this late and inconsistent recognition may have affected the existence of the phenomenon itself, since its lack of official recognition created bureaucratic barriers to public administration actions. The argument of this article is that cartographic production has kept the favela an invisible part of the city and how, especially in recent decades, this invisibility has been a political choice. The article concludes that the choice for invisibility has contributed to these large territories becoming deeply embedded in the city’s urban fabric.

Explore to Mantai, the Ancient Port-city and the International Trade-hub of Maritime Silk Road in Northern Coast of Sri Lanka

Abstract. Mantai Ancient Port City is located in Mannar, Northern Coast of Sri Lanka, archeological reserves declared by the Department of Archeology in Sri Lanka. In concern with historical international trades for almost 2000 years, Mantai had been an excellent port city as discovered by 1980s investigations. Today, this historically valued archeology reserves are being covered by various unauthorized constructions. The artifacts found in 1980s excavations figured out in further research directives to unveil the ancient cultural and social relations that had been experienced over two millennium around the globe. This research paper elaborates change detection of temporal aspects of unauthorized constructions within the respective geographical domain. Change detection researches are based on the historical maps, aerial photographs, satellite images and recent spatial data sources played over the application software. The investigation mission deployed in 1980 had unveiled evidences of civilization history and artifacts from many countries. However the archeological research and investigations were disrupted in 1983 with escalation of civil war in Northern Province. The Research elaborates visual facts and figures of the constructions erected after 1980 against their chronological measures. The research team further discusses about the tremendous variety of fields that Mantai opens to collaborate with the international research organizations.

Картографирование пещер по фотограмметрическим данным

The authors present a new approach to mapping hard-to-reach caves. It relies on a modern framework of ground-based stereo photogrammetric and geodetic surveying, aerial photography from UAVs and computer modelling. A stereo system was designed and assembled, consisting of two GoProHero9 Black cameras and a powerful artificial light source with diffusing lens, mounted on the operator`s helmet. The site for underground filming was the Syanovskaya quarry (Moscow oblast, Domodedovo rayon). The aerial survey of the cave ground part was also made with DJIAir 2S UAV; it included a ravine with a concrete well, which was the entrance to the quarry. The article deals with the processes of geodetic georeferencing, external orientation of the surface model and underground parts of the cave, as well as the cartographic materials obtained in the course of the work

Marginal Mounds and Maritime Shepherding. A Landscape Archaeological Survey of a Late Medieval Sheep Mound in the Belgian Coastal Plain

ABSTRACT The ‘Scaperie’ site in Knokke-Heist (Belgium), identified through a topographic analysis of a sixteenth-century map and located in a maritime sedimentary environment, serves as the focal point for this research. Employing a landscape archaeological approach, this study integrates multiple data sources including ceramics, historical maps, aerial photographs, LiDAR-data, geophysical surveys, and a coring survey. The dataset revealed the remnants of a anthropogenic sheep mound, evidenced by a localised microtopographic elevation, subsoil anomalies and surface artifacts associated with dairy production. More generally, this paper contributes to the understanding of the dynamic interplay between humans, sheep, and the coastal environment, offering new insights into historic land use and the archaeological characteristics of coastal mounded features. The study introduces a tripartite pattern of spatial organisation of animal husbandry, encompassing parent farms, droveways and satellite dwelling mounds. Encroached by the advancing embankments, this sheep-related taskscape was gradually marginalised both socio-economically and spatially.

FV-DLV3+: a light-weight flooded vegetation extraction method with attention-based DeepLabv3+

ABSTRACT Climate change and human activities have led to a decline in wetland vegetation and sharp reduction of wetland area, which has caused considerable damage to the hydrological cycle and ecological functions of many wetlands. Flooded vegetation (FV) is an important wetland landscape, and strengthening the research on methods of the extracting FV using remote sensing or aerial photos plays an important role in monitoring changes to wetlands. This paper used optical satellite and synthetic aperture radar satellite images as data sources, construct a set of feature variable sets suitable for FV extraction; in addition, a deep learning method based on the semantic segmentation model DeepLabv3+, FV-DLV3+, is proposed for FV extraction. The method adopts the lightweight MobileNetV2 model as the backbone feature extraction network, introduces the SegFormer network to make up for the feature information loss caused by MobileNetV2, and further captures more effective features and suppresses the interference of the background through an attention mechanism to enhance the ability to extract feature boundaries, thereby improving the accuracy of FV extraction. Taking the wetland of Poyang Lake as the experimental area, Landsat 8 and Sentinel-1A satellite data were selected as the data sources to produce a deep learning dataset, and U-Net, DeepLabv3+, Swin-Unet, Swin Transformer, and CVTNet were selected for comparison when conducting model training and validating experiments. The results show that FV-DLV3+ has an excellent effect on FV extraction of the Poyang Lake wetland, which effectively reduces the computational complexity of the process while improving extraction accuracy; the mean intersection over union of this paper’s method was higher than the five above-mentioned methods, which was 12.93% and 5.85% higher than that of DeepLabv3+ and CVTNet, respectively.

SODD-YOLOv8: an insulator defect detection algorithm based on feature enhancement and variable row convolution

Abstract To address the detection challenges in defective images of insulators in transmission lines, including tiny object size, significant scale variations, a wide variety of defects, and complex background interference. In this study, an improved insulator defect detection algorithm is proposed, based on the YOLOv8s framework and combining feature enhancement and deformable convolution techniques. Firstly, to address the image feature distortion problem caused by aerial photography, a deformable convolutional feature extraction module (DCFEM) is introduced, which is designed to enhance the model’s ability to adapt to the local geometric deformation, so as to effectively recover the distorted feature information in the image. Moreover, to enhance the detection ability of the model for small objects, a small object feature enhancement module is designed, which adopts an efficient multi-scale attention mechanism, and aims to enhance the feature extraction ability of small objects, improve the sensitivity to small-size defects, and improve the detection accuracy. Eventually, to optimize the computational efficiency of the model, the average pooling-sparse convolution-batch normalization (BN) module is proposed. This module combines average pooling, sparse convolution and BN techniques to achieve a lightweight model while maintaining a high level of feature extraction capability. Experimental results on the China power line insulator dataset show that the improved model achieves a 4.3 percentage point improvement in the mAP metric compared to YOLOv8s, and the number of parameters in the model is reduced by 10%. The proposed scheme not only improves the accuracy and efficiency of defect detection, but also reduces the demand for computational resources, thus providing a more reliable and efficient solution for insulator defect detection in practical applications.

Advances in Remote Sensing and Machine Learning Methods for Invasive Plants Study: A Comprehensive Review

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems.

Cartografía temática y recursos TIC en la enseñanza y aprendizaje de la Geografía regional de Europa

In this article, we present a classroom experience with university students in training to be secondary school geography teachers. It is based on a didactic proposal using thematic cartography (Eurostat, ESPON, ESPON-SIESTA and the Information Society Atlas in Galicia) and ICT resources (statistical sources, free picture banks, aerial photography, Google Earth and Google Maps). The proposal´s aim is to use contents from the regional geography of Europe as a base or starting point to show a series of didactic recommendations for the teaching and learning of geography and social sciences. What is more, everything is with a clear idea, from a descriptive and rote geography to an active and critical geography, in which students participate in the construction of knowledge and increase their ability to reflect on geographical issues. The results of the experience will highlight the value of these tools in secondary education. We had also previously assessed the presence of thematic cartography and ICT resources within the required official Galicia curriculum to teach geography in compulsory secondary education. The idea is to deepen the value of the curriculum in didactic code, to know what tools it offers, how it offers them and with what intentions. This solution, among other opportunities, will allow us to identify a curriculum offering a specific teaching model. On the other hand, it will also give us the opportunity to firmly rebuke all the proposals considered to be erroneous, repetitive, or of little didactic value.